use of internet in research activities

Conducting Internet Research

Considerations for participant protections when conducting internet research.

If an activity falls under the category of human subjects research, it is regulated by the federal government and Teachers College (TC) Institutional Review Board (IRB). TC IRB has provided a guide to help researchers determine if their activities can be considered human subjects research.

Internet research is a common practice of using Internet information, especially free information on the World Wide Web or Internet-based resources (e.g., discussion forums, social media), in research. This guide will cover considerations pertaining to participant protections when conducting Internet research, including:

• Private versus public spaces for exempt research
• Identifiable data available in public databases
• Minimizing risks when using sensitive Internet data
• Common Internet research approaches

The following information is from an NIH videocast . ( Odwanzy, L. (2014, May 8). Conducting Internet Research: Challenges and Strategies for IRBs [Video]. VideoCast NIH. https://videocast.nih.gov/summary.asp?Live=13932&bhcp=1 )  

Private Versus Public Spaces for Exempt Research

Federal regulations define a category of human subjects research that is exempt from IRB review as:  

“ Research that only includes interactions involving educational tests (cognitive, diagnostic, aptitude, achievement), survey procedures, interview procedures, or observation of public behavior (including visual or auditory recording) .” 

With regards to online information, if the data is publicly available (such as Census data or labor statistics), it is usually not considered human subjects research. However, if the data includes identifiable information—meaning the data can be linked back to a specific individual—then it may need to undergo IRB review. Additionally, de-identified data pulled from a private source, such as data provided by a company, may also be considered human subjects research.

Public behavior is any behavior that a subject would or could perform in public without special devices or interventions. Public behavior on the Internet, however, is more difficult to pinpoint. Federal regulations indicate that an environment may be private if a reasonable user would consider their interactions in that environment to be private. To help identify public behavior on the Internet, consider:

• Typically, posts on a private or password-protected social media profile or site are not considered public behavior.
• Even if a website is publicly available, the information on the website may be protected by other measures (e.g., community guidelines, terms of use, etc.).
• Sites that require users to pay for access to their content (e.g., purchasing a dataset) are not always considered private, even if the information is behind a paywall.
• Discussions and chats on public forums, news broadcasts, and free podcasts or videos are typically considered public communications. 
• Emails and person-to-person chat messages are often private, rather than public, communications.
• However, institutions may dictate that any activity on their devices (e.g., a company laptop or phone) is subject to review. In these cases, the institutions can limit an individual’s privacy.
• Some websites explicitly state that the interactions on their site are not to be used for research purposes.
• Other sites may not explicitly refuse research activities, but they may require users to be respectful of others’ experiences. Depending on the website, “respect” may have a variety of meanings, including respect of user privacy.
• Expectations of privacy may not always equate to the reality of privacy. 
• For example, individuals may share personal information on an open forum because there is an expectation within the community that other users will respect their privacy. However, the community guidelines may not explicitly state that their website is private.
• Forums and websites directed towards youth may require extra precautions, as the youth may be on the website with or without their guardian’s permission.
• If a user shares media on a private profile, but then that media becomes publicly available through re-posts, the media should still be considered private. It is likely that a reasonable user would expect shares on private profiles to remain private. 
• A site may only be open to certain types of users based on demographics or life experiences (e.g., cancer survivors, support groups for addiction, etc.). In these cases, a reasonable user may expect greater privacy based on the types of users they expect to interact with.

TC IRB will determine whether an Internet environment is private or public based on the IRB protocol submission.

Identifiable Data in Public Datasets

Identifiable data is information or records about a research participant that allows others to identify that person. Names, social security numbers, and bank account numbers are considered personal identifiers  and are protected under the Health Insurance Portability and Accountability Act of 1996 (HIPAA). TC IRB has a blog posted on Understanding Identifiable Data that further explains the different types of identifiers. Data that includes personal identifiers does not fall under the Exempt category.  

Other types of participant information may include indirect identifiers , such as birthdate, age, ethnicity, gender, etc. Taken alone, these pieces of information are not enough to identify any single participant. However, researchers have shown that certain combinations of these identifiers may identify participants. For example, Sweeny (2000) demonstrated that 87% of the United States population could be uniquely identified based solely on their ZIP code, gender, and date of birth.

It is important to remember that while data may be publicly available, it may still contain identifiable information. In these cases, the IRB will decide the risk to participants on a case-by-case basis. With Internet information, consider these to be possible identifiers:  

Users may include their partial or full name in a username. When collecting usernames from a site, researchers should consider replacing usernames with pseudonyms.

IP addresses are unique identifiers for devices. Researchers should be wary of pairing IP addresses with other information.

Purchase Habits

With the surge in online shopping, individuals’ unique online purchase habits are shown to be possible identifiers. 

Digital Images, Audio, & Video

Photos, audio recordings, or videos of an individual are typically considered identifiable, unless the images or audio are ascertained in a way that protects the subject’s identity.

Avatars or Profile Pictures

Although avatars and profile pictures may not include real photos of the user, it is possible that they were chosen because of a resemblance to the user.

Keystroke Dynamics or Typing Biometrics

The detailed information of an individual’s timing and rhythm when typing on a keyboard is a unique identifier. "Keystroke rhythm" measures when each key is pressed and released while a user is typing. These rhythm combinations are as unique to an individual as a fingerprint or a signature.

Minimizing Risk When Using Sensitive Internet Data 

In cases where sensitive Internet data must be used for research purposes, researchers should take precautions to ensure the safety and privacy of participants. The nature of online research increases risk to participants in some areas. Researchers should develop a plan to minimize risk in the following areas:

• Reduced Participant Contact : when research is conducted over the Internet, researchers have limited or no direct contact with subjects. This makes it more difficult for researchers to gauge subjects' reactions to the study interventions. 
• Researchers should think through multiple possibilities for interventions, debriefing, and follow-up, if applicable.
• Researcher and TC IRB contact information should be presented on the informed consent before beginning the study. This will ensure that participants know whom to contact if they have questions or concerns.
• Breach of Confidentiality: when storing or collecting data on devices connected to the Internet, there is a heightened risk for identifiable participant data to be leaked. 
• TC IRB has published a Data Security Plan  outlining best practices for securing and transmitting data. Researchers should implement these practices as they apply to their specific study.
• In the case of a breach of confidentiality, researchers must file an adverse event with TC IRB.  

Common Internet Research Approaches

The Secretary’s Advisory Committee on Human Research Protections (SACHRP) has provided examples of common Internet research practices. These include elements of research conducted over the Internet. Below are possible examples of Internet research where human subjects may be involved:  

• Existing datasets (secondary data analysis)
• Social media/blog posts
• Chat room interactions  
• Amazon Mechanical Turk
• Social media
• Patterns on social media or websites
• Evolution of privacy issues
• Spread of false information
• Online shopping patterns and personalized digital marketing
• Online interventions such as “nudging"

Increased Internet use for research requires researchers and IRBs to become familiar with Internet research-related topics and concerns. Research submitted to the IRB will be reviewed on a case-by-case basis. The Institutional Review Board at Teachers College will make the final determination of whether a study requires review. Researchers should email  [email protected] if they have any questions or concerns about their study design and whether it should be IRB reviewed.

Institutional Review Board

Address: Russell Hall, Room 13

* Phone: 212-678-4105 * Email:   [email protected]

Appointments are available by request . Make sure to have your IRB protocol number (e.g., 19-011) available.  If you are unable to access any of the downloadable resources, please contact  OASID via email [email protected] .

• Table of Contents
• Random Entry
• Chronological
• Editorial Information
• About the SEP
• Editorial Board
• How to Cite the SEP
• Special Characters
• Advanced Tools
• Support the SEP
• PDFs for SEP Friends
• Make a Donation
• SEPIA for Libraries
• Entry Contents

Bibliography

Academic tools.

• Friends PDF Preview
• Author and Citation Info
• Back to Top

Internet Research Ethics

There is little research that is not impacted in some way on or through the Internet. The Internet, as a field, a tool, and a venue, has specific and far-reaching ethical issues. Internet research ethics is a subdiscipline that fits across many disciplines, ranging from social sciences, arts and humanities, medical/biomedical, and natural sciences. Extant ethical frameworks, including consequentialism , deontology , virtue ethics , and feminist ethics , have contributed to the ways in which ethical issues in Internet research are considered and evaluated.

Conceptually and historically, Internet research ethics is most related to computer and information ethics and includes such ethical issues as participant knowledge and consent, data privacy, security, anonymity and confidentiality, and integrity of data, intellectual property issues, and community, disciplinary, and professional standards or norms. Throughout the Internet’s evolution, there has been continued debate whether there are new ethical dilemmas emerging, or if the existing dilemmas are similar to dilemmas in other research realms (Elgesem 2002; Walther 2002; Ess & AoIR 2002; Marhkam & Buchanan 2012). These debates are similar to philosophical debates in computer and information ethics. For example, many years ago, James Moor (1985) asked “what is special about computers” in order to understand what, if anything, is unique ethically. Reminding us, however, that research itself must be guided by ethical principles, regardless of technological intervention, van Heerden et al. (2020) and Sloan et al. (2020) stress that the “fundamental principles of conducting ethical social research remain the same” (Ess & AoIR 2002; King 1996; Samuel and Buchanan, 2020).

Yet, as the Internet has evolved into a more social and communicative tool and venue, the ethical issues have shifted from purely data-driven to more human-centered. “On-ground” or face-to-face analogies, however, may not be applicable to online research. For example, the concept of the public park has been used as a site where researchers might observe others with little ethical controversy, but online, the concepts of public versus private are much more complex (SACHRP 2013). Thus, some scholars suggest that the specificity of Internet research ethics calls for new regulatory and/or professional and disciplinary guidance. For these reasons, the concept of human subjects research policies and regulation, informs this entry, which will continue discussions around ethical and methodological complexity, including personal identifiability, reputational risk and harm, notions of public space and public text, ownership, and longevity of data as they relate to Internet research. Specifically, the emergence of the social web raises issues around subject or participant recruitment practices, tiered informed consent models, and protection of various expectations and forms of privacy in an ever-increasing world of diffused and ubiquitous technologies. Additional ethical concerns center on issues of anonymity and confidentiality of data in spaces where researchers and their subjects may not fully understand the terms and conditions of those venues or tools, challenges to data integrity as research projects can be outsourced or crowdsourced to online labor marketplaces, and jurisdictional issues as more research is processed, stored, and disseminated via cloud computing or in remote server locales, presenting myriad legal complexities given jurisdictional differences in data laws. Further, the dominance of big data research has continued across research spaces, with the notions of “real-world data” and pervasive computing readily accepted and used in all disciplines. The ease of access and availability to use big data sets in myriad ways has enabled AI (artificial intelligence) and ML (machine learning) to grow as standard tools for researchers.

As a result, researchers using the Internet as a tool for and/or a space of research—and their research ethics boards (REBs), also known as institutional review boards (IRBs) in the United States or human research ethics committees (HRECs) in other countries such as Australia—have been confronted with a series of new ethical questions: What ethical obligations do researchers have to protect the privacy of subjects engaging in activities in “public” Internet spaces? What are such public spaces? Is there any reasonable expectation of privacy in an era of pervasive and ubiquitous surveillance and data tracking? How is confidentiality or anonymity assured online? How is and should informed consent be obtained online? How should research on minors be conducted, and how do you prove a subject is not a minor? Is deception (pretending to be someone you are not, withholding identifiable information, etc.) an acceptable online norm or a harm? How is “harm” possible to someone existing in an online space? How identifiable are individuals in large data sets? Do human subjects protections apply to big data? As more industry-sponsored research takes place, what ethical protections exist outside of current regulatory structures? As laws, such as the EU’s General Data Protection Regulation (GDPR 2016) are enacted, what are the global implications for data privacy and individual rights?

A growing number of scholars have explored these and related questions (see, for example, Bromseth 2002; Bruckman 2006; Buchanan 2004; Buchanan & Ess 2008; Johns, Chen & Hall 2003; Kitchin 2003, 2008; King 1996; Mann 2003; Markham & Baym 2008; McKee & Porter 2009; Thorseth 2003; Ess 2016; Zimmer & Kinder-Kurlanda (eds.) 2017; Samuel & Buchanan, 2020), scholarly associations have drafted ethical guidelines for Internet research (Ess & Association of Internet Researchers 2002; Markham, Buchanan, and AoIR 2012; franzke et al., 2020; Kraut et al. 2004), and non-profit scholarly and scientific agencies such as AAAS (Frankel & Siang 1999) are confronting the myriad of ethical concerns that Internet research poses to researchers and research ethics boards (REBs).

Given that over 50% of the world population uses the Internet, and that 97% of the world population now lives within reach of a mobile cellular signal and 93% within reach of a 3G (or higher) network (International Telecommunications Union, 2019), continued exploration of the ethical issues related to research in this heavily mediated environment is critical.

1. Definitions

2. human subjects research, 3. history and development of ire as a discipline, 4.1 privacy, 4.2 recruitment, 4.3.1 minors and consent, 4.4 cloud computing and research ethics, 4.5 big data considerations, 4.6 internet research and industry ethics, 5. research ethics boards guidelines, cited in entry, laws and government documents, professional standards, journals, forums, and blogs, other resources, related entries.

The commonly accepted definition of Internet research ethics (IRE) has been used by Buchanan and Ess (2008, 2009), Buchanan (2011), and Ess & Association of Internet Researchers (AoIR) (2002):

IRE is defined as the analysis of ethical issues and application of research ethics principles as they pertain to research conducted on and in the Internet. Internet-based research, broadly defined, is research which utilizes the Internet to collect information through an online tool, such as an online survey; studies about how people use the Internet, e.g., through collecting data and/or examining activities in or on any online environments; and/or, uses of online datasets, databases, or repositories.

These examples were broadened in 2013 by the United States Secretary’s Advisory Committee to the Office for Human Research Protections (SACHRP 2013), and included under the umbrella term Internet Research:

• Research studying information that is already available on or via the Internet without direct interaction with human subjects (harvesting, mining, profiling, scraping, observation or recording of otherwise-existing data sets, chat room interactions, blogs, social media postings, etc.)
• Research that uses the Internet as a vehicle for recruiting or interacting, directly or indirectly, with subjects (Self-testing websites, survey tools, Amazon Mechanical Turk, etc.)
• Research about the Internet itself and its effects (use patterns or effects of social media, search engines, email, etc.; evolution of privacy issues; information contagion; etc.)
• Research about Internet users: what they do, and how the Internet affects individuals and their behaviors Research that utilizes the Internet as an interventional tool, for example, interventions that influence subjects’ behavior
• Others (emerging and cross-platform types of research and methods, including m-research (mobile))
• Recruitment in or through Internet locales or tools, for example social media, push technologies

A critical distinction in the definition of Internet research ethics is that between the Internet as a research tool versus a research venue. The distinction between tool and venue plays out across disciplinary and methodological orientations. As a tool, Internet research is enabled by search engines, data aggregators, digital archives, application programming interfaces (APIs), online survey platforms, and crowdsourcing platforms. Internet-based research venues include such spaces as conversation applications (instant messaging and discussion forums, for example), online multiplayer games, blogs and interactive websites, and social networking platforms.

Another way of conceptualizing the distinction between tool and venue comes from Kitchin (2008), who has referred to a distinction in Internet research using the concepts of “engaged web-based research” versus “non-intrusive web-based research:”

Non-intrusive analyses refer to techniques of data collection that do not interrupt the naturally occurring state of the site or cybercommunity, or interfere with premanufactured text. Conversely, engaged analyses reach into the site or community and thus engage the participants of the web source (2008: 15).

These two constructs provide researchers with a way of recognizing when considering of human subject protections might need to occur. McKee and Porter (2009), as well as Banks and Eble (2007) provide guidance on the continuum of human-subjects research, noting a distinction between person-based versus text-based. For example, McKee and Porter provide a range of research variables (public/private, topic sensitivity, degree of interaction, and subject vulnerability) which are useful in determining where on the continuum of text-based versus how person-based the research is, and whether or not subjects would need to consent to the research (2009: 87–88).

While conceptually useful for determining human subjects participation, the distinction between tool and venue or engaged versus non-intrusive web-based research is increasingly blurring in the face of social media and their third-party applications. Buchanan (2016) has conceptualized three phases or stages of Internet research, and the emergence of social media characterize the second phase, circa 2006–2014. The concept of social media entails

A group of Internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of user-generated content (Kaplan & Haenlein 2010: 61).

A “social network site” is a category of websites with profiles, semi-persistent public commentary on the profile, and a traversable publicly articulated social network displayed in relation to the profile.

This collapse of tool and venue can be traced primarily to the increasing use of third-party sites and applications such as Facebook, Twitter, or any of the myriad online research tools where subject or participant recruitment, data collection, data analysis, and data dissemination can all occur in the same space. With these collapsing boundaries, the terms of “inter-jurisdictional coordination” (Gilbert 2009: 3) are inherently challenging; Gilbert has specifically argued against the terms of use or end-user license agreement stipulations in virtual worlds, noting that such agreements are often “flawed”, as they rely on laws and regulations from a specific locale and attempt to enforce them in a non place-based environment. Nonetheless, researchers now make frequent use of data aggregation tools, scraping data from user profiles or transaction logs, harvesting data from Twitter streams, or storing data on cloud servers such as Dropbox only after agreeing to the terms of service that go along with those sites. The use of such third party applications or tools changes fundamental aspects of research, oftentimes displacing the researcher or research team as the sole owner of their data. These unique characteristics implicate concepts and practicalities of privacy, consent, ownership, and jurisdictional boundaries.

A key moment that typified and called attention to many of these concerns emerged with the 2014 Facebook Emotional Contagion study (Booth, 2014). By virtue of agreeing to Facebook’s Terms of Service, did users consent to participation in research activities? Should there have been a debriefing after the experiment? How thoroughly did a university research ethics board review the study? Should industry-sponsored research undergo internal ethics review? In response to the outcry of the Contagion study, Ok Cupid’s Christian Rudder (2014 [ OIR ]) defended these sorts of experiments, noting

We noticed recently that people didn’t like it when Facebook “experimented” with their news feed. Even the FTC is getting involved. But guess what, everybody: if you use the Internet, you’re the subject of hundreds of experiments at any given time, on every site. That’s how websites work.

The phenomenon of the social web forces an ongoing negotiation between researchers and their data sources, as seen in the Facebook contagion study and the subsequent reaction to it. Moreover, with the growing use and concentration of mobile devices, the notion of Internet research is expanding with a movement away from a “place-based” Internet to a dispersed reality. Data collection from mobile devices has increased exponentially. For example, mobile devices enable the use of synchronous data collection and dissemination from non-place based environments. Researchers using cloud-enabled applications can send and receive data to and from participants synchronously. The impact of such research possibilities for epidemiological research is staggering for its scientific potential while demanding for the concurrent ethical challenges, as we are seeing with mobile-based COVID-19 research (Drew et al., 2020) and the sampling of subjects' current behaviors and experiences in real-time (Hubach et al., forthcoming). As Internet research has grown from a niche methodology into a nearly ubiquitous and often invisible practice, the traditional concepts of human subjects research require careful consideration.

The practical, professional, and theoretical implications of human subjects protections has been covered extensively in scholarly literature, ranging from medical/biomedical to social sciences to computing and technical disciplines (see Beauchamp & Childress 2008; Emanual et al. 2003; PRIM&R et al. 2021; Sieber 1992; Wright 2006). Relevant protections and regulations continue to receive much attention in the face of research ethics violations (see, for example, Skloot 2010, on Henrietta Lacks; the U.S. Government’s admission and apology to the Guatemalan Government for STD testing in the 1940s (BBC 2011); and Gaw & Burns 2011, on how lessons from the past might inform current research ethics and conduct).

The history of human subjects protections (Sparks 2002 [see Other Internet Resources aka OIR ]) grew out of atrocities such as Nazi human experimentation during World War II, which resulted in the Nuremberg Code (1947); subsequently followed by the Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects (World Medical Association 1964/2008). Partially in response to the Tuskegee syphilis experiment, an infamous clinical study conducted between 1932 and 1972 by the U.S. Public Health Service studying the natural progression of untreated syphilis in rural African-American men in Alabama under the guise of receiving free health care from the government, the U.S. Department of Health and Human Services put forth a set of basic regulations governing the protection of human subjects (45 C.F.R. § 46) (see the links in the Other Internet Resources section, under Laws and Government Documents). This was later followed by the publication of the “Ethical Principles and Guidelines for the Protection of Human Subjects of Research” by the National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, known as the Belmont Report (NCPHSBBR 1979). The Belmont Report identifies three fundamental ethical principles for all human subjects research: Respect for Persons, Beneficence, and Justice.

To ensure consistency across federal agencies in the United States context in human subjects protections, in 1991, the Federal Policy for the Protection of Human Subjects, also known as the “Common Rule” was codified; the Revised Common Rule was released in the Federal Register on 19 January 2017, and went into effect 19 July 2018. Similar regulatory frameworks for the protection of human subjects exist across the world, and include, for example, the Canadian Tri-Council, the Australian Research Council, The European Commission, The Research Council of Norway and its National Committee for Research Ethics in the Social Sciences and Humanities (NESH 2006; NESH 2019), and the U.K.’s NHS National Research Ethics Service and the Research Ethics Framework (REF) of the ESRC (Economic and Social Research Council) General Guidelines, and the Forum for Ethical Review Committees in Asia and the Western Pacific (FERCAP).

In the United States, the various regulatory agencies who have signed on to the Common Rule (45 C.F.R. 46 Subpart A) have not issued formal guidance on Internet research (see the links in the Other Internet Resources section, under Laws and Government Documents). The Preamble to the Revised Rule referenced significant changes in the research environment, recognizing a need to broaden the scope of the Rule. However, substantial changes to the actual Rule in regards to Internet research in its broadest context, were minimal.

For example, the Preamble states:

This final rule recognizes that in the past two decades a paradigm shift has occurred in how research is conducted. Evolving technologies—including imaging, mobile technologies, and the growth in computing power—have changed the scale and nature of information collected in many disciplines. Computer scientists, engineers, and social scientists are developing techniques to integrate different types of data so they can be combined, mined, analyzed, and shared. The advent of sophisticated computer software programs, the Internet, and mobile technology has created new areas of research activity, particularly within the social and behavioral sciences (Federal Register 2017 and HHS 2017).

Modest changes to the definition of human subjects included changing “data” to “information” and “biospecimens;” the definition now reads:

(e) (1) Human subject means a living individual about whom an investigator (whether professional or student) conducting research: (i) Obtains information or biospecimens through intervention or interaction with the individual, and uses, studies, or analyzes the information or biospecimens; or (ii) Obtains, uses, studies, analyzes, or generates identifiable private information or identifiable biospecimens. (2) Intervention includes both physical procedures by which information or biospecimens are gathered (e.g., venipuncture) and manipulations of the subject or the subject's environment that are performed for research purposes. (3) Interaction includes communication or interpersonal contact between investigator and subject. (4) Private information includes information about behavior that occurs in a context in which an individual can reasonably expect that no observation or recording is taking place, and information that has been provided for specific purposes by an individual and that the individual can reasonably expect will not be made public (e.g., a medical record). (5) Identifiable private information is private information for which the identity of the subject is or may readily be ascertained by the investigator or associated with the information. (6) An identifiable biospecimen is a biospecimen for which the identity of the subject is or may readily be ascertained by the investigator or associated with the biospecimen (45 C.F.R. § 46.102 (2018)).

However, the Revised Rule does have a provision that stands to be of import in regards to Internet research; the Rule calls for implementing departments or agencies to,

[(e)(7)] (i) Upon consultation with appropriate experts (including experts in data matching and re-identification), reexamine the meaning of “identifiable private information”, as defined in paragraph (e)(5) of this section, and “identifiable biospecimen”, as defined in paragraph (e)(6) of this section. This reexamination shall take place within 1 year and regularly thereafter (at least every 4 years). This process will be conducted by collaboration among the Federal departments and agencies implementing this policy. If appropriate and permitted by law, such Federal departments and agencies may alter the interpretation of these terms, including through the use of guidance. (ii) Upon consultation with appropriate experts, assess whether there are analytic technologies or techniques that should be considered by investigators to generate “identifiable private information”, as defined in paragraph (e)(5) of this section, or an “identifiable biospecimen”, as defined in paragraph (e)(6) of this section. This assessment shall take place within 1 year and regularly thereafter (at least every 4 years). This process will be conducted by collaboration among the Federal departments and agencies implementing this policy. Any such technologies or techniques will be included on a list of technologies or techniques that produce identifiable private information or identifiable biospecimens. This list will be published in the Federal Register after notice and an opportunity for public comment. The Secretary, HHS, shall maintain the list on a publicly accessible Web site (45 C.F.R. § 46.102 (2018)).

As of this writing, there has not yet been a reexamination of the concepts of “identifiable private information” or “identifiable biospecimens”. However, as data analytics, AI, and machine learning continue to expose ethical issues in human subjects research, we expect to see engaged discussion at the federal level and amongst research communities (PRIM&R 2021). Those discussions may refer to previous conceptual work by Carpenter and Dittrich (2012) and Aycock et al. (2012) that is concerned with risk and identifiability. Secondary uses of identifiable, private data, for example, may pose downstream harms, or unintentional risks, causing reputational or informational harms. Reexaminations of “identifiable private information” can not occur without serious consideration of risk and “human harming research”. Carpenter and Dittrich (2012) encourage

“Review boards should transition from an informed consent driven review to a risk analysis review that addresses potential harms stemming from research in which a researcher does not directly interact with the at-risk individuals” (p. 4) as “[T]his distance between researcher and affected individual indicates that a paradigm shift is necessary in the research arena. We must transition our idea of research protection from ‘human subjects research’ to ‘human harming research’” (p. 14). [ 1 ]

Similarly, Aycock et al. (2012) assert that

Researchers and boards must balance presenting risks related to the specific research with risks related to the technologies in use. With computer security research, major issues around risk arise, for society at large especially. The risk may not seem evident to an individual but in the scope of security research, larger populations may be vulnerable. There is a significant difficulty in quantifying risks and benefits, in the traditional sense of research ethics….An aggregation of surfing behaviors collected by a bot presents greater distance between researcher and respondent than an interview done in a virtual world between avatars. This distance leads us to suggest that computer security research focus less concern around human subjects research in the traditional sense and more concern with human harming research (p. 3, italics original).

These two conceptual notions are relevant for considering emergent forms of identities or personally identifiable information (PII) such as avatars, virtual beings, bots, textual and graphical information. Within the Code of Federal Regulations (45 C.F.R. § 46.102(f) 2009): New forms of representations are considered human subjects if PII about living individuals is obtained. PII can be obtained by researchers through scraping data sources, profiles or avatars, or other pieces of data made available by the platform. Fairfield agrees: “An avatar, for example, does not merely represent a collection of pixels—it represents the identity of the user” (2012: 701).

The multiple academic disciplines already long engaged in human subjects research (medicine, sociology, anthropology, psychology, communication) have established ethical guidelines intended to assist researchers and those charged with ensuring that research on human subjects follows both legal requirements and ethical practices. But with research involving the Internet—where individuals increasingly share personal information on platforms with porous and shifting boundaries, where both the spread and aggregation of data from disparate sources has become the norm, and where web-based services, and their privacy policies and terms of service statements, morph and evolve rapidly—the ethical frameworks and assumptions traditionally used by researchers and REBs are frequently challenged.

Research ethics boards themselves are increasingly challenged with the unique ethical dimensions of internet-based research protocols. In a 2008 survey of U.S. IRBs, less than half of the ethical review boards identified internet-based research was “an area of concern or importance” at that time, and only 6% had guidelines or checklists in place for reviewing internet-based research protocols (Buchanan & Ess 2009). By 2015, 93% of IRBs surveyed acknowledged that are ethical issues unique to research using “online data”, yet only 55% said they felt their IRBs are well versed in the technical aspects of online data collection, and only 57% agreed that their IRB has the expertise to stay abreast of changes in online technology. IRBs are now further challenged with the growth of big data research (see §4.5 below ), which increasingly relies on large datasets of personal information generated via social media, digital devices, or other means often hidden from users. A 2019 study of IRBs revealed only 25% felt prepared to evaluate protocols relying on big data, and only 6% had tools sufficient for considering this emerging area of internet research (Zimmer & Chapman 2020). Further, after being presented various hypothetical research scenarios utilizing big data and asked how their IRB would likely review such a protocol, numerous viewpoints different strongly in many cases. Consider the following scenario:

Researchers plan to scrape public comments from online newspaper pages to predict election outcomes. They will aggregate their analysis to determine public sentiment. The researchers don’t plan to inform commenters, and they plan to collect potentially-identifiable user names. Scraping comments violates the newspaper’s terms of service.

18% of respondents indicated their IRB would view this as exempt, 21% indicated expedited review, 33% suggested it would need full board review, while 28% did not think this was even human subjects research that would fall under their IRB’s purview (Zimmer & Chapman 2020). This points to potential gaps and inconsistencies in how IRBs review the ethical implications of big data research protocols.

An extensive body of literature has developed since the 1990s around the use of the Internet for research (S. Jones 1999; Hunsinger, Klastrup, & Allen (eds.) 2010; Consalvo & Ess (eds.) 2011; Zimmer & Kinder-Kurlanda (eds.) 2017), with a growing emphasis on the ethical dimensions of Internet research.

A flurry of Internet research, and explicit concern for the ethical issues concurrently at play in it, began in the mid-1990s. In 1996, Storm King recognized the growing use of the Internet as a venue for research. His work explored the American Psychological Association’s guidelines for human subjects research with emergent forms of email, chat, listservs, and virtual communities. With careful attention to risk and benefit to Internet subjects, King offered a cautionary note:

When a field of study is new, the fine points of ethical considerations involved are undefined. As the field matures and results are compiled, researchers often review earlier studies and become concerned because of the apparent disregard for the human subjects involved (1996: 119).

The 1996 issue of Information Society dedicated to Internet research is considered a watershed moment, and included much seminal research still of impact and relevance today (Allen 1996; Boehlefeld 1996; Reid 1996).

Sherry Turkle’s 1997 Life on the Screen: Identity in the Age of the Internet called direct attention to the human element of online game environments. Moving squarely towards person-based versus text-based research, Turkle pushed researchers to consider human subjects implications of Internet research. Similarly, Markham’s Life Online: Researching Real Experience in Virtual Space (1998) highlighted the methodological complexities of online ethnographic studies, as did Jacobson’s 1999 methodological treatment of Internet research. The “field” of study changed the dynamics of researcher-researched roles, identity, and representation of participants from virtual spaces. Markham’s work in qualitative online research has been influential across disciplines, as research in nursing, psychology, and medicine has found the potential of this paradigm for online research (Flicker et al. 2004; Eysenbach & Till 2001; Seaboldt & Kupier 1997; Sharf 1997).

Then, in 1999, the American Association for the Advancement of Science (AAAS), with a contract from the U.S. Office for Protection from Research Risks (now known as the Office for Human Research Protections), convened a workshop, with the goal of assessing the alignment of traditional research ethics concepts to Internet research. The workshop acknowledged

The vast amount of social and behavioral information potentially available on the Internet has made it a prime target for researchers wishing to study the dynamics of human interactions and their consequences in this virtual medium. Researchers can potentially collect data from widely dispersed population sat relatively low cost and in less time than similar efforts in the physical world. As a result, there has been an increase in the number of Internet studies, ranging from surveys to naturalistic observation (Frankel & Siang 1999: 1).

In the medical/biomedical contexts, Internet research has grown rapidly. Also in 1999, Gunther Eysenbach wrote the first editorial to the newly formed Journal of Medical Internet Research . There were three driving forces behind the inception of this journal, and Eysenbach called attention to the growing social and interpersonal aspects of the Internet:

First, Internet protocols are used for clinical information and communication. In the future, Internet technology will be the platform for many telemedical applications. Second, the Internet revolutionizes the gathering, access and dissemination of non-clinical information in medicine: Bibliographic and factual databases are now world-wide accessible via graphical user interfaces, epidemiological and public health information can be gathered using the Internet, and increasingly the Internet is used for interactive medical education applications. Third, the Internet plays an important role for consumer health education, health promotion and teleprevention. (As an aside, it should be emphasized that “health education” on the Internet goes beyond the traditional model of health education, where a medical professional teaches the patient: On the Internet, much “health education” is done “consumer-to-consumer” by means of patient self support groups organizing in cyberspace. These patient-to-patient interchanges are becoming an important part of healthcare and are redefining the traditional model of preventive medicine and health promotion).

With scholarly attention growing and with the 1999 AAAS report (Frankel & Siang 1999) calling for action, other professional associations took notice and began drafting statements or guidelines, or addendum to their extant professional standards. For example, The Board of Scientific Affairs (BSA) of the American Psychological Association established an Advisory Group on Conducting Research on the Internet in 2001; the American Counseling Association’s 2005 revision to its Code of Ethics; the Association of Internet Researchers (AoIR) Ethics Working Group Guidelines, the National Committee for Research Ethics in the Social Sciences and the Humanities (NESH Norway), among others, have directed researchers and review boards to the ethics of Internet research, with attention to the most common areas of ethical concern (see OIR for links).

While many researchers focus on traditional research ethics principles, conceptualizations of Internet research ethics depend on disciplinary perspectives. Some disciplines, notably from the arts and humanities, posit that Internet research is more about context and representation than about “human subjects”, suggesting there is no intent, and thus minimal or no harm, to engage in research about actual persons. The debate has continued since the early 2000s. White (2002) argued against extant regulations that favored or privileged specific ideological, disciplinary and cultural prerogatives, which limit the freedoms and creativity of arts and humanities research. For example, she notes that the AAAS report “confuses physical individuals with constructed materials and human subjects with composite cultural works”, again calling attention to the person versus text divide that has permeated Internet research ethics debates. Another example of disciplinary differences comes from the Oral History Association, which acknowledged the growing use of the Internet as a site for research:

Simply put, oral history collects memories and personal commentaries of historical significance through recorded interviews. An oral history interview generally consists of a well-prepared interviewer questioning an interviewee and recording their exchange in audio or video format. Recordings of the interview are transcribed, summarized, or indexed and then placed in a library or archives. These interviews may be used for research or excerpted in a publication, radio or video documentary, museum exhibition, dramatization or other form of public presentation. Recordings, transcripts, catalogs, photographs and related documentary materials can also be posted on the Internet (Ritchie 2003: 19).

While the American Historical Association (A. Jones 2008) has argued that such research be “explicitly exempted” from ethical review board oversight, the use of the Internet could complicate such a stance if such data became available in public settings or available “downstream” with potential, unforeseeable risks to reputation, economic standing, or psychological harm, should identification occur.

Under the concept of text rather than human subjects, Internet research rests on arguments of publication and copyright; consider the venue of a blog, which does not meet the definition of human subject as in 45 C.F.R. § 46.102f (2009), as interpreted by most ethical review boards. A researcher need not obtain consent to use text from a blog, as it is generally considered publicly available, textual, published material. This argument of the “public park” analogy that has been generally accepted by researchers is appropriate for some Internet venues and tools, but not all: Context, intent, sensitivity of data, and expectations of Internet participants were identified in 2004 by Sveninngsson as crucial markers in Internet research ethics considerations.

By the mid-2000s, with three major anthologies published, and a growing literature base, there was ample scholarly literature documenting IRE across disciplines and methodologies, and subsequently, there was anecdotal data emerging from the review boards evaluating such research. In search of empirical data regarding the actual review board processes of Internet research from a human subjects perspective, Buchanan and Ess surveyed over 700 United States ethics review boards, and found that boards were primarily concerned with privacy, data security and confidentiality, and ensuring appropriate informed consent and recruitment procedures (Buchanan & Ess 2009; Buchanan & Hvizdak 2009).

In 2008, the Canadian Tri-Council’s Social Sciences and Humanities Research Ethics Special Working Committee: A Working Committee of the Interagency Advisory Panel on Research Ethics was convened (Blackstone et al. 2008); and in 2010, a meeting at the Secretary’s Advisory Committee to the Office for Human Research Protections highlighted Internet research (SACHRP 2010). Such prominent professional organizations as the Public Responsibility in Medicine and Research (PRIM&R) and the American Educational Research Association (AERA) have begun featuring Internet research ethics regularly at their conferences and related publications.

Recently, disciplines not traditionally involved in human subjects research have begun their own explorations of IRE. For example, researchers in computer security have actively examined the tenets of research ethics in CS and ICT (Aycock et al. 2012; Dittrich, Bailey, & Dietrich 2011; Carpenter & Dittrich 2012; Buchanan et al. 2011). Notably, the U.S. Federal Register requested comments on “The Menlo Report” in December 2011, which calls for a commitment by computer science researchers to the three principles of respect for persons, beneficence, and justice, while also adding a fourth principle on respect for law and public interest (Homeland Security 2011). SIGCHI, an international society for professionals, academics, and students interested in human-technology and human-computer interaction (HCI), has increasingly focused on how IRE applies to work in their domain (Frauenberger et al. 2017; Fiesler et al. 2018).

4. Key Ethical Issues in Internet Research

Principles of research ethics dictate that researchers must ensure there are adequate provisions to protect the privacy of subjects and to maintain the confidentiality of any data collected. A violation of privacy or breach of confidentiality presents a risk of serious harm to participants, ranging from the exposure of personal or sensitive information, the divulgence of embarrassing or illegal conduct, or the release of data otherwise protected under law.

Research ethics concerns around individual privacy is often expressed in terms of the level of linkability of data to individuals, and the potential harms from disclosure of information As Internet research has grown in complexity and computational sophistication, ethics concerns have focused on current and future uses of data, and the potential downstream harms that could occur. Protecting research participants’ privacy and confidentiality is typically achieved through a combination of research tactics and practices, including engaging in data collection under controlled or anonymous environments, the scrubbing of data to remove personally identifiable information (PII), or the use of access restrictions and related data security methods. And, the specificity and characteristics of the data will often dictate if there are regulatory considerations, in addition to the methodological considerations around privacy and confidentiality. For example, personally identifiable information (PII) typically demands the most stringent protections. The National Institutes of Health (NIH), for example, defines PII as:

any information about an individual maintained by an agency, including, but not limited to, education, financial transactions, medical history, and criminal or employment history and information which can be used to distinguish or trace an individual’s identity, such as their name, SSN, date and place of birth, mother’s maiden name, biometric records, etc., including any other personal information that is linked or linkable to an individual (NIH 2010).

Typically, examples of identifying pieces of information have included personal characteristics (such as date of birth, place of birth, mother’s maiden name, gender, sexual orientation, and other distinguishing features and biometrics information, such as height, weight, physical appearance, fingerprints, DNA and retinal scans), unique numbers or identifiers assigned to an individual (such as a name, address, phone number, social security number, driver’s license number, financial account numbers), and descriptions of physical location (GIS/GPS log data, electronic bracelet monitoring information).

The 2018 EU General Data Protection Regulation lays out the legal and regulatory requirements for data use across the EU. Mondschein & Monda (2018) provides a thorough discussion on the different types of data that are considered in the GDPR: Personal data, such as names, identification numbers, location data, and so on; Special categories of personal data, such as race or ethic origin, political opinions, or religious beliefs; Pseudonymous data, referring to data that has been altered so the subject cannot be directly identified without having further information; Anonymous data, information which does not relate to an identifiable natural person or to personal data rendered anonymous in such a manner that the data subject is not or no longer identifiable. They also advise researchers to consider

data protection issues at an early stage of a research project is of great importance specifically in the context of large-scale research endeavours that make use of personal data (2018: 56).

Internet research introduces new complications to these longstanding definitions and regulatory frameworks intended to protect subject privacy. For example, researchers increasingly are able to collect detailed data about individuals from sources such as Facebook, Twitter, blogs or public email archives, and these rich data sets can more easily be processed, compared, and combined with other data (and datasets) available online. In numerous cases, both researchers and members of the general public have been able to re-identify individuals by analyzing and comparing such datasets, using data-fields as benign as one’s zip code (Sweeny 2002), random Web search queries (Barbaro & Zeller 2006), or movie ratings (Narayanan & Shmatikov 2008) as the vital key for reidentification of a presumed anonymous user. Prior to widespread Internet-based data collection and processing, few would have considered one’s movie ratings or zipcode as personally-identifiable. Yet, these cases reveal that merely stripping traditional “identifiable” information such as a subject’s name, address, or social security number is no longer sufficient to ensure data remains anonymous (Ohm 2010), and requires the reconsideration of what is considered “personally identifiable information” (Schwartz & Solove 2011). This points to the critical distinction between data that is kept confidential versus data that is truly anonymous. Increasingly, data are rarely completely anonymous, as researchers have routinely demonstrated they can often reidentify individuals hidden in “anonymized” datasets with ease (Ohm 2010). This reality places new pressure on ensuring datasets are kept, at the least, suitably confidential through both physical and computational security measures. These measures may also include requirements to store data in “clean rooms”, or in non-networked environments in an effort to control data transmission.

Similarly, new types of data often collected in Internet research might also be used to identify a subject within a previously-assumed anonymous dataset. For example, Internet researchers might collect Internet Protocol (IP) addresses when conducting online surveys or analyzing transaction logs. An IP address is a unique identifier that is assigned to every device connected to the Internet; in most cases, individual computers are assigned a unique IP address, while in some cases the address is assigned to a larger node or Internet gateway for a collection of computers. Nearly all websites and Internet service providers store activity logs that link activity with IP addresses, in many cases, eventually to specific computers or users. Current U.S. law does not hold IP addresses to be personally identifiable information, while other countries and regulatory bodies do. For example, the European Data Privacy Act at Article 29, holds that IP addresses do constitute PII. Buchanan et al. (2011), note, however, that under the U.S. Civil Rights Act, for the purposes of the HIPAA Act, [ 2 ] IP addresses are considered a form of PII (45 C.F.R. § 164.514 2002). [ 3 ] There could potentially be a reconsideration by other federal regulatory agencies over IP addresses as PII, and researchers and boards will need to be attentive should such change occur.

A similar complication emerges when we consider the meaning of “private information” within the context of Internet-based research. U.S. federal regulations define “private information” as:

[A]ny information about behavior that occurs in a context in which an individual can reasonably expect that no observation or recording is taking place, and information that has been provided for specific purposes by an individual and that the individual can reasonably expect will not be made public (for example, a medical record) (45 C.F.R. § 46.102(f) 2009).

This standard definition of “private information” has two key components. First, private information is that which subjects reasonably expect is not normally monitored or collected. Second, private information is that which subjects reasonably expect is not typically publicly available. Conversely, the definition also suggests the opposite is true: if users cannot reasonably expect data isn’t being observed or recorded, or they cannot expect data isn’t publicly available, then the data does not rise to the level of “private information” requiring particular privacy protections. Researchers and REBs have routinely worked with this definition of “private information” to ensure the protection of individuals’ privacy.

These distinctions take on greater weight, however, when considering the data environments and collection practices common with Internet-based research. Researchers interested in collecting or analyzing online actions of subjects—perhaps through the mining of online server logs, the use of tracking cookies, or the scraping of social media profiles and feeds—could argue that subjects do not have a reasonable expectation that such online activities are not routinely monitored since nearly all online transactions and interactions are routinely logged by websites and service providers. Thus, online data trails might not rise to the level of “private information”. However, numerous studies have indicated that average Internet users have incomplete understandings of how their activities are routinely tracked, and the related privacy practices and policies of the sites they visit (Hoofnagle & King 2008 [ OIR ]; Milne & Culnan 2004; Tsai et al. 2006). Hudson and Bruckman (2005) conducted empirical research on users’ expectations and understandings of privacy, finding that participants’ expectations of privacy within public chatrooms conflicted with what was actually a very public online space. Rosenberg (2010) examined the public/private distinction in the realm of virtual worlds, suggesting researchers must determine what kind of social norms and relations predominate an online space before making assumptions about the “publicness” of information shared within. Thus, it remains unclear whether Internet users truly understand if and when their online activity is regularly monitored and tracked, and what kind of reasonable expectations truly exist. This ambiguity creates new challenges for researchers and REBs when trying to apply the definition of “private information” to ensure subject privacy is properly addressed (Zimmer 2010).

This complexity in addressing subject privacy in Internet research is further compounded with the rise of social networking as a place for the sharing of information, and a site for research. Users increasingly share more and more personal information on platforms like Facebook or Twitter. For researchers, social media platforms provide a rich resource for study, and much of the content is available to be viewed and downloaded with minimal effort. Since much of the information posted to social media sites is publicly viewable, it thus fails to meet the standard regulatory definition of “private information”. Therefore, researchers attempting to collect and analyze social media postings might not treat the data as requiring any particular privacy considerations. Yet, social media platforms represent a complex environment of social interaction where users are often required to place friends, lovers, colleagues, and minor acquaintances within the same singular category of “friends”, where privacy policies and terms of service are not fully understood (Madejski et al. 2011), and where the technical infrastructures fail to truly support privacy projections (Bonneau & Preibush 2010) and regularly change with little notice (Stone 2009 [ OIR ]; Zimmer 2009 [ OIR ]). As a result, it is difficult to understand with any certainty what a user’s intention was when posting an item onto a social media platform (Acquisti & Gross 2006). The user may have intended the post for a private group but failed to completely understand how to adjust the privacy settings accordingly. Or, the information might have previously been restricted to only certain friends, but a change in the technical platform suddenly made the data more visible to all.

Ohm (2010) warns that

the utility and privacy of data are linked, and so long as data is useful, even in the slightest, then it is also potentially reidentifiable (2010: 1751).

With the rapid growth of Internet-based research, Ohm’s concern becomes even more dire. The traditional definitions and approaches to understanding the nature of privacy, anonymity, and precisely what kind of information deserves protection becomes strained, forcing researchers and REBs to consider more nuanced theories of privacy (Nissenbaum 2009) and approaches to respecting and projecting subject privacy (Markham 2012; Zimmer 2010).

Depending on the type of Internet research being carried out, recruitment of participants may be done in a number of ways. As with any form of research, the study population or participants are selected for specific purposes (i.e., an ethnographic study of a particular group on online game players), or, can be selected from a range of sampling techniques (i.e., a convenience sample gleaned from the users of Amazon’s Mechanical Turk crowdsourcing platform [ 4 ] ). In the U.S. context, a recruitment plan is considered part of the informed consent process, and as such, any recruitment script or posting must be reviewed and approved by an REB prior to posting or beginning solicitation (if the project is human subjects research). Further, the selection of participants must be impartial and unbiased, and any risks and benefits must be justly distributed. This concept is challenging to apply in Internet contexts, in which populations are often self-selected and can be exclusive, depending on membership and access status, as well as the common disparities of online access based on economic and social variables. Researchers also face recruitment challenges due to online subjects’ potential anonymity, especially as it relates to the frequent use of pseudonyms online, having multiple or alternative identities online, and the general challenges of verifying a subject’s age and demographic information. Moreover, basic ethical principles for approaching and recruiting participants involve protecting their privacy and confidentiality. Internet research can both maximize these protections, as an individual may never be known beyond a screen name or avatar existence; or, conversely, the use of IP addresses, placement of cookies, availability and access to more information than necessary for the research purposes, may minimize the protections of privacy and confidentiality.

Much recruitment is taking place via social media; examples include push technologies, a synchronous approach in which a text or tweet is sent from a researcher to potential participants based on profile data, platform activity, or geolocation. Other methods of pull technologies recruitment include direct email, dedicated web pages, YouTube videos, direct solicitation via “stickies” posted on fora or web sites directing participants to a study site, or data aggregation or scraping data for potential recruitment. Regardless of the means used, researchers must follow the terms of the site—from the specific norms and nuances governing a site or locale to the legal issues in terms of service agreements. For example, early pro-anorexia web sites (see Overbeke 2008) were often treated as sensitive spaces deserving spcicial consideration, and researchers were asked to respect the privacy of the participants and not engage in research (Walstrom 2004). In the gaming context, Reynolds and de Zwart (2010) ask:

Has the researcher disclosed the fact that he or she is engaged in research and is observing/interacting with other players for the purposes of gathering research data? How does the research project impact upon the community and general game play? Is the research project permitted under the Terms of Service?

Colvin and Lanigan (2005: 38) suggest researchers

Seek permission from Web site owners and group moderators before posting recruitment announcements, Then, preface the recruitment announcement with a statement that delineates the permission that has been granted, including the contact person and date received. Identify a concluding date (deadline) for the research study and make every effort to remove recruitment postings, which often become embedded within Web site postings.

Barratt and Lenton, among others, agree:

It is critical, therefore, to form partnerships with online community moderators by not only asking their permission to post the request, but eliciting their feedback and support as well (2010: 71).

Mendelson (2007) and Smith and Leigh (1997) note that recruitment notices need to contain more information than the typical flyers or advertisements used for newspaper advertisements. Mentioning the approval of moderators is important for establishing authenticity, and so is providing detailed information about the study and how to contact both the researchers and the appropriate research ethics board.

Given the array of techniques possible for recruitment, the concept of “research spam” requires attention. The Council of American Survey Research warns

Research Organizations should take steps to limit the number of survey invitations sent to targeted respondents by email solicitations or other methods over the Internet so as to avoid harassment and response bias caused by the repeated recruitment and participation by a given pool (or panel) of data subjects (CASRO 2011: I.B.3).

Ultimately, researchers using Internet recruitment measures must ensure that potential participants are getting enough information in both the recruitment materials and any subsequent consent documents. Researchers must ensure that recruitment methods do not lead to an individual being identified without their permission, and if such identification is possible, are there significant risks involved?

4.3 Informed Consent

As the cornerstone of human subjects protections, informed consent means that participants are voluntarily participating in the research with adequate knowledge of relevant risks and benefits. Providing informed consent typically includes the researcher explaining the purpose of the research, the methods being used, the possible outcomes of the research, as well as associated risks or harms that the participants might face. The process involves providing the recipient clear and understandable explanations of these issues in a concise way, providing sufficient opportunity to consider them and enquire about any aspect of the research prior to granting consent, and ensuring the subject has not been coerced into participating. Gaining consent in traditional research is typically done verbally or in writing, either in a face-to-face meeting where the researcher reviews the document, through telephone scripts, through mailed documents, fax, or video, and can be obtained with the assistance of an advocate in the case of vulnerable populations. Most importantly, informed consent was built on the ideal of “process” and the verification of understanding, and thus, requires an ongoing communicative relationship between and among researchers and their participants. The emergence of the Internet as both a tool and a venue for research has introduced challenges to this traditional approach to informed consent.

In most regulatory frameworks, there are instances when informed consent might be waived, or the standard processes of obtaining informed consent might be modified, if approved by a research ethics board. [ 5 ] Various forms of Internet research require different approaches to the consent process. Some standards have emerged, depending on venue (i.e., an online survey platform versus a private Facebook group). However, researchers are encouraged to consider waiver of consent and/or documentation, if appropriate, by using the flexibilities of their extant regulations.

Where consent is required but documentation has been waived by an ethical review board, a “portal” can be used to provide consent information. For example, a researcher may send an email to the participant with a link a separate portal or site information page where information on the project is contained. The participant can read the documentation and click on an “I agree” submission. Rosser et al. (2010) recommend using a “chunked” consent document, whereby individuals can read specific sections, agree, and then continue onwards to completion of the consent form, until reaching the study site.

In addition to portals, researchers will often make use of consent cards or tokens; this alleviates concerns that unannounced researcher presence is unacceptable, or, that a researcher’s presence is intrusive to the natural flow and movement of a given locale. Hudson and Bruckman (2004, 2005) highlighted the unique challenges in gaining consent in chat rooms, while Lawson (2004) offers an array of consent possibilities for synchronous computer-mediated communication. There are different practical challenges in the consent process in Internet research, given the fluidity and temporal nature of Internet spaces.

If documentation of consent is required, some researchers have utilized alternatives such as electronic signatures, which can range from a simple electronic check box to acknowledge acceptance of the terms to more robust means of validation using encrypted digital signatures, although the validity of electronic signatures vary by jurisdiction.

Regardless of venue, informed consent documents are undergoing changes in the information provided to research participants. While the basic elements of consent remain intact, researchers must now acknowledge with less certainty specific aspects of their data longevity, risks to privacy, confidentiality and anonymity (see §4.1 Privacy, above ), and access to or ownership of data. Research participants must understand that their terms of service or end user license agreement consent is distinct from their consent to participate in research. And, researchers must address and inform participants/subjects about potential risk of data intrusion or misappropriation of data if subsequently made public or available outside of the confines of the original research. Statements should be revised to reflect such realities as cloud storage (see §4.4 below ) and data sharing.

For example, Aycock et al. (2012: 141) describe a continuum of security and access statements used in informed consent documents:

• “No others will have access to the data”
• “Anonymous identifiers will be used during all data collection and analysis and the link to the subject identifiers will be stored in a secure manner”
• “Data files that contain summaries of chart reviews and surveys will only have study numbers but no data to identify the subject. The key [linking] subject names and these study identifiers will be kept in a locked file”
• “Electronic data will be stored on a password protected and secure computer that will be kept in a locked office. The software ‘File Vault’ will be used to protect all study data loaded to portable laptops, flash drives or other storage media. This will encode all data… using Advanced Encryption Standard with 128-bit keys (AES-128)”

This use of encryption in the last statement may be necessary in research including sensitive data, such as medical, sexual, health, financial, and so on. Barratt and Lenton (2010), in their research on illicit drug use and online forum behaviors, also provide guidance about use of secure transmission and encryption as part of the consent process.

In addition to informing participants about potential risks and employing technological protections, NIH-funded researchers whose work includes projects with identifiable, sensitive information will automatically be issued a Certificate of Confidentiality:

CoCs protect the privacy of research subjects by prohibiting disclosure of identifiable, sensitive research information to anyone not connected to the research except when the subject consents or in a few other specific situations (NIH 2021 [ OIR ]).

However, these do not protect against release of data outside of the U.S. Given the reality of Internet research itself, which inherently spans borders, new models may be in order to ensure confidentiality of data and protections of data. Models of informed consent for traditional international research are fundamentally challenging due to cultural specificity and norms (Annas 2009; Boga et al. 2011; Krogstad et al. 2010); with Internet research, where researchers may be unaware of the specific location of an individual, consent takes on significantly higher demands. While current standards of practice show that consent models stem from the jurisdiction of the researcher and sponsoring research institution, complications arise in the face of age verification, age of majority/consent, reporting of adverse effects or complaints with the research process, and authentication of identity. Various jurisdictional laws around privacy are relevant for the consent process; a useful tool is Forrester’s Data Privacy Heat Map, which relies on in-depth analyses of the data privacy-related laws and cultures of countries around the world, helping researchers design appropriate approaches to privacy and data protection given the particular context (see OIR ).

In addition, as more federal agencies and funding bodies across the globe encourage making research data publicly-available (i.e., NSF, NIH, Wellcome Trust, Research Councils U.K.), the language used in consent documents will change accordingly to represent this intended longevity of data and opportunities for future, unanticipated use. Given the ease with which Internet data can flow between and among Internet venues, changes in the overall accessibility of data might occur (early “private” newsgroup conversations were made “publicly searchable” when Google bought DejaNews), and reuse and access by others is increasingly possible with shared datasets. Current data sharing mandates must be considered in the consent process. Alignment between a data sharing policy and an informed consent document is imperative. Both should include provisions for appropriate protection of privacy, confidentiality, security, and intellectual property.

There is general agreement in the U.S. that individual consent is not necessary for researchers to use publicly available data, such as public Twitter feeds. Recommendations were made by The National Human Subjects Protection Advisory Committee (NHRPAC) in 2002 regarding publicly available data sets (see OIR ). Data use or data restriction agreements are commonly used and set the parameters of use for researchers.

The U.K. Data Archive (2011 [ OIR ]) provides guidance on consent and data sharing:

When research involves obtaining data from people, researchers are expected to maintain high ethical standards such as those recommended by professional bodies, institutions and funding organisations, both during research and when sharing data. Research data — even sensitive and confidential data — can be shared ethically and legally if researchers pay attention, from the beginning of research, to three important aspects: • when gaining informed consent, include provision for data sharing • where needed, protect people’s identities by anonymising data • consider controlling access to data These measures should be considered jointly. The same measures form part of good research practice and data management, even if data sharing is not envisioned. Data collected from and about people may hold personal, sensitive or confidential information. This does not mean that all data obtained by research with participants are personal or confidential.

Data sharing made public headlines in 2016 when a Danish researcher released a data set comprised of scraped data from nearly 70,000 users of the OkCupid online dating site. The data set was highly reidentifiable and included potentially sensitive information, including usernames, age, gender, geographic location, what kind of relationship (or sex) they’re interested in, personality traits, and answers to thousands of profiling questions used by the site. The researcher claimed the data were public and thus, such sharing and use was unproblematic. Zimmer (2016) was among many privacy and ethics scholars who critiqued this stance.

The Danish researchers did not seek any form of consent or debriefing on the collection and use of the data, nor did they have any ethics oversight. Many researchers and ethics boards are, however, attempting to mitigate some of these ethical concerns by including blanket statements in their consent processes, indicating such precautions for research participants. For example,

I understand that online communications may be at greater risk for hacking, intrusions, and other violations. Despite these possibilities, I consent to participate.

A more specific example comes from the Canadian context when researchers propose to use specific online survey tools hosted in the United States; REBs commonly recommend the following type language for use in informed consent documents:

Please note that the online survey is hosted by Company ABC which is a web survey company located in the U.S.A. All responses to the survey will be stored and accessed in the U.S.A. This company is subject to U.S. Laws, in particular, to the U.S. Patriot Act/Domestic Security Enhancement Act that allows authorities access to the records that your responses to the questions will be stored and accessed in the U.S.A. The security and private policy for Company ABC can be viewed at http://…/. [ 6 ]

Researchers are also encouraged to review the Terms of Use and Terms of Service of the application that are being used, demonstrating its details to the REB in the application and informing participants of such details in the informed consent form or script. Researchers are also encouraged to consider broader contextual factors of the data source and research goals when weighing the possible violation of a platform’s Terms of Service (Fiesler, Beard, & Keegan 2020).

Internet research poses particular challenges to age verification, assent and consent procedures, and appropriate methodological approaches with minors. Age of consent varies across countries, states, communities, and locales of all sorts. For research conducted or supported by U.S. federal agencies bound by the Common Rule, children are

persons who have not attained the legal age for consent [18, in the U.S.] to treatments or procedures involved in the research, under the applicable law of the jurisdiction in which the research will be conducted (45 C.F.R. § 46.402(a) 2009).

Goldfarb (2008) provides an exhaustive discussion of age of majority across the U.S. states, with a special focus on clinical research , noting children must be seven or older to assent to participation (see 45 C.F.R. § 46 Subpart D 2009).

Spriggs (2010), from the Australian context, notes that while no formal guidance exists on Internet research and minors under the National Statement , she advises:

Parental consent may be needed when information is potentially identifiable. Identifiable information makes risks to individuals higher and may mean that the safety net of parental consent is preferable. There is also a need to consider whether seeking parental consent would make things worse e.g., by putting a young person from a dysfunctional home at risk or result in disclosure to the researcher of additional identifying information about the identity and location of the young person. Parental consent may be “contrary to the best interests” of the child or young person when it offers no protection or makes matters worse (2010: 30).

To assist with the consent process, age verification measures can be used. These can range from more technical software applications to less formal knowledge checks embedded in an information sheet or consent document. Multiple confirmation points (asking for age, later asking for year of birth, etc.) are practical measures for researchers. Depending on the types of data, sensitivity of data, use of data, researchers and boards will carefully construct the appropriate options for consent, including waiver of consent, waiver of documentation, and/or waiver of parental consent.

Recent developments in cloud computing platforms have led to unique opportunities—and ethical challenges—for researchers. Cloud computing describes the deployment of computing resources via the Internet, providing on-demand, flexible, and scalable computing from remote locations. Examples include web-based email and calendaring services provided by Google or Yahoo, online productivity platforms like Google Docs or Microsoft Office 365, online file storage and sharing platforms like Dropbox or Box.net, and large-scale application development and data processing platforms such as Google Apps, Facebook Developers Platform, and Amazon Web Services.

Alongside businesses and consumers, researchers have begun utilizing cloud computing platforms and services to assist in various tasks, including subject recruitment, data collection and storage, large-scale data processing, as well as communication and collaboration (Allan 2011 [ OIR ]; X. Chen et al. 2010 [ OIR ]); Simmhan et al. 2008; Simmhan et al. 2009).

As reliance on cloud computing increases among researchers, so do the ethical implications. Among the greatest concerns is ensuring data privacy and security with cloud-based services. For researchers sharing datasets online for collaborative processing and analysis, steps must be taken to ensure only authorized personnel have access to the online data that might contain PII, but also that suitable encryption is used for data transfer and storage, and that the cloud service provider maintains sufficient security to prevent breaches. Further, once research data is uploaded to a third-party cloud provider, attention must be paid to the terms of service for the contracted provider to determine what level of access to the data, if any, might be allowed to advertisers, law enforcement, or other external agents.

Alongside the privacy and security concerns, researchers also have an ethical duty of data stewardship which is further complicated when research data is placed in the cloud for storage or processing. Cloud providers might utilize data centers spread across the globe, meaning research data might be located outside the United States, and its legal jurisdictions. Terms of service might grant cloud providers a license to access and use research data for purposes not initially intended or approved of by the subjects involved. Stewardship may require the prompt and complete destruction of research data, a measure complicated if a cloud provider has distributed and backed-up the data across multiple locations.

A more unique application of cloud computing for research involves the crowdsourcing of data analysis and processing functions, that is, leveraging the thousands of users of various online products and services to complete research related tasks remotely. Examples include using a distributed network of video game players to assist in solving protein folding problems (Markoff 2010), and leveraging Amazon’s Mechanical Turk crowdsourcing marketplace platform to assist with large scale data processing and coding functions that cannot be automated (Conley & Tosti-Kharas 2014; J. Chen et al. 2011). Using cloud-based platforms can raise various critical ethical and methodological issues.

First, new concerns over data privacy and security emerge when research tasks are widely distributed across a global network of users. Researchers must take great care in ensuring research data containing personal or sensitive information isn’t accessible by outsourced labor, or that none of the users providing crowdsourced labor are able to aggregate and store their own copy of the research dataset. Second, crowdsourcing presents ethical concerns over trust and validity of the research process itself. Rather than a local team of research assistants usually under a principal investigator’s supervision and control, crowdsourcing tends to be distributed beyond the direct management or control of the researcher, providing less opportunity to ensure sufficient training for the required tasks. Thus, researchers will need to create additional means of verifying data results to confirm tasks are completed properly and correctly.

Two additional ethical concerns with crowdsourcing involve labor management and authorship. Mechanical Turk users were not originally intended to be research subjects, first and foremost. However, researchers using Mechanical Turks must ensure that the laborers on the other end of the cloud-based relationship are not being exploited, that they are legally eligible to be working for hire, and that the incentives provided are real, meaningful, and appropriate (Scholz 2008; Williams 2010 [ OIR ).

Finally, at the end of a successful research project utilizing crowdsourcing, a researcher may be confronted with the ethical challenge of how to properly acknowledge the contributions made by (typically anonymous) laborers. Ethical research requires the fair and accurate description of authorship. Disciplines vary as to how to report relative contributions made by collaborators and research assistants, and this dilemma increases when crowdsourcing is used to assist with the research project.

Algorithmic processing is a corollary of big data research, and newfound ethical considerations have emerged. From “algorithmic harms” to “predictive analytics”, the power of today’s algorithms exceeds long-standing privacy beliefs and norms. Specifically, the National Science and Technology Council note:

“Analytical algorithms” as algorithms for prioritizing, classifying, filtering, and predicting. Their use can create privacy issues when the information used by algorithms is inappropriate or inaccurate, when incorrect decisions occur, when there is no reasonable means of redress, when an individual’s autonomy is directly related to algorithmic scoring, or when the use of predictive algorithms chills desirable behavior or encourages other privacy harms. (NSTC 2016: 18).

While the concept of big data is not new, and the term has been in technical discourses since the 1990s, public awareness and response to big data research is much more recent. Following the rise of social media-based research, Buchanan (2016) has delineated the emergence of “big data”-based research from 2012 to the present, with no signs of an endpoint.

Big data research is challenging for research ethics boards, often presenting what the computer ethicist James Moor would call “conceptual muddles”: the inability to properly conceptualize the ethical values and dilemmas at play in a new technological context. Subject privacy, for example, is typically protected within the context of research ethics through a combination of various tactics and practices, including engaging in data collection under controlled or anonymous environments, limiting the personal information gathered, scrubbing data to remove or obscure personally identifiable information, and using access restrictions and related data security methods to prevent unauthorized access and use of the research data itself. The nature and understanding of privacy become muddled, however, in the context of big data research, and as a result, ensuring it is respected and protected in this new domain becomes challenging.

For example, the determination of what constitutes “private information”—and thus triggering particular privacy concerns—becomes difficult within the context of big data research. Distinctions within the regulatory definition of “private information”—namely, that it only applies to information which subjects reasonably expect is not normally monitored or collected and not normally publicly available—become less clearly applicable when considering the data environments and collection practices that typify big data research, such as the wholesale scraping of Facebook news feed content or public OKCupid accounts.

When considered through the lens of the regulatory definition of “private information”, social media postings are often considered public, especially when users take no visible, affirmative steps to restrict access. As a result, big data researchers might conclude subjects are not deserving of particular privacy consideration. Yet, the social media platforms frequently used for big data research purposes represent a complex environment of socio-technical interactions, where users often fail to understand fully how their social activities might be regularly monitored, harvested, and shared with third parties, where privacy policies and terms of service are not fully understood and change frequently, and where the technical infrastructures and interfaces are designed to make restricting information flows and protecting one’s privacy difficult.

As noted in §4.1 above it becomes difficult to confirm a user’s intention when sharing information on a social media platform, and whether users recognize that providing information in a social environment also opens it up for widespread harvesting and use by researchers. This uncertainty in the intent and expectations of users of social media and internet-based platforms—often fueled by the design of the platforms themselves—create numerous conceptual muddles in our ability to properly alleviate potential privacy concerns in big data research.

The conceptual gaps that exist regarding privacy and the definition of personally identifiable information in the context of big data research inevitably lead to similar gaps regarding when informed consent is necessary. Researchers mining Facebook profile information or public Twitter streams, for example, typically argue that no specific consent is necessary due to the fact the information was publicly available. It remains unknown whether users truly understood the technical conditions under which they made information visible on these social media platforms or if they foresaw their data being harvested for research purposes, rather than just appearing onscreen for fleeting glimpses by their friends and followers (Fiesler & Proferes, 2018). In the case of the Facebook emotional contagion experiment (Kramer, Guillory, & Hancock 2014), the lack of obtaining consent was initially rationalized through the notion that the research appeared to have been carried out under Facebook’s extensive terms of service, whose data use policy, while more than 9,000 words long, does make passing mention to “research”. It was later revealed, however, that the data use policy in effect when the experiment was conducted never mentioned “research” at all (Hill 2014).

Additional ethical concerns have arisen surrounding the large scale data collection practices connected to machine learning and the development of artificial intelligence. For example, negative public attention have surrounded algorithms designed to infer sexual orientation from photographs and facial recognition algorithms trained on videos of transgender people. In both cases, ethical concerns have been raised about both the purpose of these algorithms and the fact that the data that trained them (dating profile photos and YouTube videos, respectively) was “public” but collected from potentially vulnerable populations without consent (Metcalf 2017; Keyes 2019). While those building AI systems cannot always control the conditions under which the data they utilize is collected, their increased use of big datasets captured from social media or related sources raises a number of concerns beyond what typically is considered part of the growing focus on AI ethics: fairness, accountability and transparency in AI can only be fully possible when data collection is achieved in a fair, ethical, and just manner (Stahl & Wright 2018; Kerry 2020).

The Facebook emotional contagion experiment, discussed above, is just one example in a larger trend of big data research conducted outside of traditional university-based research ethics oversight mechanisms. Nearly all online companies and platforms analyze data and test theories that often rely on data from individual users. Industry-based data research, once limited to marketing-oriented “A/B testing” of benign changes in interface designs or corporate communication messages, now encompasses information about how users behave online, what they click and read, how they move, eat, and sleep, the content they consume online, and even how they move about their homes. Such research produces inferences about individuals’ tastes and preferences, social relations, communications, movements, and work habits. It implies pervasive testing of products and services that are an integral part of intimate daily life, ranging from connected home products to social networks to smart cars. Except in cases where they are partnering with academic institutions, companies typically do not put internal research activities through a formal ethical review process, since results are typically never shared publicly and the perceived impact on users is minimal.

The growth of industry-based big data research, however, presents new risks to individuals’ privacy, on the one hand, and to organizations’ legal compliance, reputation, and brand, on the other hand. When organizations process personal data outside of their original context, individuals may in some cases greatly benefit, but in other cases may be surprised, outraged, or even harmed. Soliciting consent from affected individuals can be impractical: Organizations might collect data indirectly or based on identifiers that do not directly match individuals’ contact details. Moreover, by definition, some non-contextual uses—including the retention of data for longer than envisaged for purposes of a newly emergent use—may be unforeseen at the time of collection. As Crawford and Schultz (2014) note,

how does one give notice and get consent for innumerable and perhaps even yet-to-be-determined queries that one might run that create “personal data”? (2014: 108)

With corporations developing vast “living laboratories” for big data research, research ethics has become a critical component of the design and oversight of these activities. For example, in response to the controversy surrounding the emotional contagion experiment, Facebook developed an internal ethical review process that, according to its facilitators,

leverages the company’s organizational structure, creating multiple training opportunities and research review checkpoints in the existing organizational flow (Jackman & Kanerva 2016: 444).

While such efforts are important and laudable, they remain open for improvement. Hoffmann (2016), for example, has criticized Facebook for launching

an ethics review process that innovates on process but tells us little about the ethical values informing their product development.

Further, in their study of employees doing the work of ethics inside of numerous Silicon Valley companies, Metcalf and colleagues found considerable tension between trying to resolve thorny ethical dilemmas that emerge within an organization’s data practices and the broader business model and corporate logic that dominates internal decision-making (Metcalf, Moss, & boyd 2019).

While many researchers and review boards across the world work without formal guidance, many research ethics boards have developed guidelines for Internet research. While many such guidelines exist, the following provides examples for researchers preparing for an REB review, or for boards developing their own policies.

• Bard College (New York) Guidelines for Internet Research
• Loyola University Chicago Policy for Online Survey Research Involving Human Participants
• Penn State Guidelines for Computer- and Internet-Based Research Involving Human Participants
• U.K. Data Archive Further Resources
• University of California-Berkeley Data Security and Human Research Data Risk Assessment Matrix (pdf)
• University of Connecticut Guidance for Computer and Internet-Based Research Involving Human Participants

Additional resources are found in Other Internet Resources below.

• Acquisti, Alessandro and Ralph Gross, 2006, “Imagined Communities: Awareness, Information Sharing, and Privacy on the Facebook”, in Privacy Enhancing Technologies: PET 2006 , George Danezis and Philippe Golle (eds.), (Lecture Notes in Computer Science 4258), Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 36–58. doi:10.1007/11957454_3
• Allen, Christina, 1996, “What’s Wrong with the ‘Golden Rule’? Conundrums of Conducting Ethical Research in Cyberspace”, The Information Society , 12(2): 175–188. doi:10.1080/713856146
• Annas, George J., 2009, “Globalized Clinical Trials and Informed Consent”, New England Journal of Medicine , 360(20): 2050–2053. doi:10.1056/NEJMp0901474
• Aycock, John, Elizabeth Buchanan, Scott Dexter, and David Dittrich, 2012, “Human Subjects, Agents, or Bots: Current Issues in Ethics and Computer Security Research”, in Financial Cryptography and Data Security , George Danezis, Sven Dietrich, and Kazue Sako (eds.), (Lecture Notes in Computer Science 7126), Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 138–145. doi:10.1007/978-3-642-29889-9_12
• Banks, Will and Michelle Eble, 2007, “Digital Spaces, Online Environments, and Human Participant Research: Interfacing with Institutional Review Boards”, in Digital Writing Research: Technologies, Methodologies, and Ethical Issues , Heidi A. McKee and Dànielle Nicole DeVoss (eds.), Cresskill, NJ: Hampton Press, pp. 27–47.
• Barbaro, Michael and Tom Zeller Jr., 2006, “A Face Is Exposed for AOL Searcher No. 4417749”, The New York Times , 9 August 2006, pp. A1.
• Barratt, Monica Jane and Simon Lenton, 2010, “Beyond Recruitment? Participatory Online Research with People Who Use Drugs”, International Journal of Internet Research Ethics , 3(1): 69–86. [ Barratt and Lenton 2010 available online ]
• BBC, 2011, “US Scientists ‘Knew Guatemala Syphilis Tests Unethical’”, BBC News, 30 August 2011, sec. Latin America & Caribbean. [ BBC 2011 available online ]
• Beauchamp, Tom L. and James F. Childress, 2008, Principles of Biomedical Ethics , Oxford: Oxford University Press.
• Blackstone, Mary, Lisa Given, Joseph Levy, Michelle McGinn, Patrick O’Neill, Ted Palys, and Will van den Hoonaard, 2008, Extending the Spectrum: The TCPS and Ethical Issues Involving Internet-Based Research , Interagency Advisory Panel and Secretariat on Research Ethics, Ottawa, Canada. [ Blackstone et al. 2008 available online ]
• Boehlefeld, Sharon Polancic, 1996, “Doing the Right Thing: Ethical Cyberspace Research”, The Information Society , 12(2): 141–152. doi:10.1080/713856136
• Boga, Mwanamvua, Alun Davies, Dorcas Kamuya, Samson M. Kinyanjui, Ester Kivaya, Francis Kombe, Trudie Lang, Vicki Marsh, Bibi Mbete, Albert Mlamba, et al., 2011, “Strengthening the Informed Consent Process in International Health Research through Community Engagement: The KEMRI-Wellcome Trust Research Programme Experience”, PLoS Medicine , 8(9): e1001089. doi:10.1371/journal.pmed.1001089
• Bonneau, Joseph and Sören Preibusch, 2010, “The Privacy Jungle: On the Market for Data Protection in Social Networks”, in Economics of Information Security and Privacy , Tyler Moore, David Pym, and Christos Ioannidis (eds.), Boston: Springer US, pp. 121–167. doi:10.1007/978-1-4419-6967-5_8
• Booth, Robert, 2014, “Facebook reveals news feed experiment to control emotions”, The Guardian , 29 June 2014. [ Booth 2014 available online ]
• Bromseth, Janne C. H., 2002, “Public Places: Public Activities? Methodological Approaches and Ethical Dilemmas in Research on Computer-mediated Communication Contexts”, in Researching ICTs in Context , Andrew Morrison (ed.), InterMedia Report 3/2002, Oslo: University of Oslo, pp. 33–61. [ Bromseth 2002 available online ]
• Brothers, Kyle Bertram and Ellen Wright Clayton, 2010, “‘Human Non-Subjects Research’: Privacy and Compliance”, The American Journal of Bioethics , 10(9): 15–17. doi:10.1080/15265161.2010.492891
• Bruckman, Amy, 2006, “Teaching Students to Study Online Communities Ethically”, Journal of Information Ethics , 15(2): 82–98. doi:10.3172/JIE.15.2.82
• Buchanan, Elizabeth A. (ed.), 2004, Readings in Virtual Research Ethics: Issues and Controversies , Hershey, PA: Information Science Publishing.
• –––, 2006, “Introduction: Internet Research Ethics at a Critical Juncture”, Journal of Information Ethics , 15(2): 14–17. doi:10.3172/JIE.15.2.14
• –––, 2011, “Internet Research Ethics: Past, Present, and Future”, in Consalvo and Ess 2011: 83–108. doi:10.1002/9781444314861.ch5
• –––, 2016, “Ethics in Digital Research”, in Handbuch Soziale Praktiken und Digitale Alltagswelten , Heidrun Friese, Gala Rebane, Marcus Nolden, and Miriam Schreiter (eds.), Wiesbaden: Springer Fachmedien Wiesbaden, pp. 1–9. doi:10.1007/978-3-658-08460-8_47-1
• Buchanan, Elizabeth A. and Charles M. Ess, 2008, “Internet Research Ethics: The Field and Its Critical Issues”, in The Handbook of Information and Computer Ethics , Kenneth Einar Himma and Herman T. Tavani (eds.), Hoboken, NJ: John Wiley & Sons, Inc., pp. 273–292. doi:10.1002/9780470281819.ch11
• –––, 2009, “Internet Research Ethics and the Institutional Review Board: Current Practices and Issues”, ACM SIGCAS Computers and Society , 39(3): 43–49. doi:10.1145/1713066.1713069
• Buchanan, Elizabeth A. and Erin E. Hvizdak, 2009, “Online Survey Tools: Ethical and Methodological Concerns of Human Research Ethics Committees”, Journal of Empirical Research on Human Research Ethics , 4(2): 37–48. doi:10.1525/jer.2009.4.2.37
• Buchanan, Elizabeth, John Aycock, Scott Dexter, David Dittrich, and Erin Hvizdak, 2011, “Computer Science Security Research and Human Subjects: Emerging Considerations for Research Ethics Boards”, Journal of Empirical Research on Human Research Ethics , 6(2): 71–83. doi:10.1525/jer.2011.6.2.71
• Carpenter, Katherine J. and David Dittrich, 2012, “Bridging the Distance: Removing the Technology Buffer and Seeking Consistent Ethical Analysis in Computer Security Research”, in Digital Ethics: Research & Practice (Digital Formations 85), Don Heider and Adrienne Massanari (eds.), New York: Peter Lang, pp. 1–29.
• [CASRO] Council of American Survey Research, 2011, “CASRO Code of Standards and Ethics for Survey Research”, First adopted 1977 and revised since. [ CASRO code available online ]
• Chen, Jenny J., Natala J. Menezes, and Adam D. Bradley, 2011, “Opportunities for Crowdsourcing Research on Amazon Mechanical Turk”, Interfaces , 5(3). [ J. Chen, Menezes, and Bradley 2011 available online ]
• Colvin, Jan and Jane Lanigan, 2005, “Ethical Issues and Best Practice Considerations for Internet Research”, Journal of Family and Consumer Sciences , 97(3): 34–39.
• Conley, Caryn and Jennifer Tosti-Kharas, 2014, “Crowdsourcing Content Analysis for Managerial Research”, Management Decision , 52(4): 675–688. doi:10.1108/MD-03-2012-0156
• Consalvo, Mia and Charles Ess (eds.), 2011, The Handbook of Internet Studies , Oxford: Wiley-Blackwell. doi:10.1002/9781444314861
• Crawford, Kate and Jason Schultz, 2014, “Big Data and Due Process: Toward a Framework to Redress Predictive Privacy Harms”, Boston College Law Review , 55(1): 93–128.
• Dittrich, David, Michael Bailey, and Sven Dietrich, 2011, “Building an Active Computer Security Ethics Community”, IEEE Security & Privacy Magazine , 9(4): 32–40. doi:10.1109/MSP.2010.199
• Drew, David A., Long H. Nguyen, Claire J. Steves, Cristina Menni, Maxim Freydin, Thomas Varsavsky, Carole H. Sudre, M. Jorge Cardoso, Sebastien Ourselin, Jonathan Wolf, et al., 2020, “Rapid Implementation of Mobile Technology for Real-Time Epidemiology of COVID-19”, Science , 368(6497): 1362–1367. doi:10.1126/science.abc0473
• Elgesem, Dag, 2002, “What Is Special about the Ethical Issues in Online Research?”, Ethics and Information Technology , 4(3): 195–203. doi:10.1023/A:1021320510186
• Emanuel, Ezekiel J., Robert A. Crouch, John D. Arras, Jonathan D. Moreno, and Christine Grady (eds.), 2003, Ethical and Regulatory Aspects of Clinical Research: Readings and Commentary , Baltimore: Johns Hopkins University Press.
• Ess, Charles, 2016, “Phronesis for machine ethics? Can robots perform ethical judgments?”, Frontiers in Artificial Intelligence and Applications , 290: 386–389. doi:10.3233/978-1-61499-708-5-386
• Ess, Charles and the Association of Internet Researchers (AoIR) Ethics Working committee, 2002, “Ethical Decision-Making and Internet Research: Recommendations from the AoIR Ethics Working Committee”, Approved by the AoIR, 27 November 2002. [ Ess and AoIR 2002 available online ]
• Eysenbach, Gunther, 1999, “Welcome to the Journal of Medical Internet Research”, Journal of Medical Internet Research , 1(1): e5. doi:10.2196/jmir.1.1.e5
• Eysenbach, Gunther and James E. Till, 2001, “Ethical Issues in Qualitative Research on Internet Communities”, BMJ , 323(7321): 1103–1105. doi:10.1136/bmj.323.7321.1103
• Fairfield, Joshua A., 2012, “Avatar Experimentation: Human Subjects Research in Virtual Worlds”, U.C. Irvine Law Review , 2: 695–772.
• Federal Register, 2011, “Submission for Review and Comment: ‘The Menlo Report: Ethical Principles Guiding Information and Communication Technology Research’” (“Menlo Report”) for the Department of Homeland Security (DHS), Science and Technology, Cyber Security Division (CSD), Protected Repository for the Defense of Infrastructure Against Cyber Threats (PREDICT)”, 28 December 2011, Volume 76, Number 249, Docket No. DHS-2011-0074. [ Federal Register 2011 available online ]
• –––, 2017, “Federal Policy for the Protection of Human Subjects”, 19 January 2017, Volume 82, Number 12 [ Federal Register 2017 available online ]
• Fiesler, Casey, Nathan Beard, and Brian C. Keegan, 2020, “No Robots, Spiders, or Scrapers: Legal and Ethical Regulation of Data Collection Methods in Social Media Terms of Service”, Proceedings of the International AAAI Conference on Web and Social Media , 14: 187–196. [ Fiesler, Beard, and Keegan 2020 available online ]
• Fiesler, Casey, Jeff Hancock, Amy Bruckman, Michael Muller, Cosmin Munteanu, and Melissa Densmore, 2018, “Research Ethics for HCI: A Roundtable Discussion”, in Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems , , Montreal QC Canada: ACM, 1–5. doi:10.1145/3170427.3186321
• Fiesler, Casey and Nicholas Proferes, 2018, “‘Participant’ Perceptions of Twitter Research Ethics”, Social Media + Society , 4(1), first online 10 March 2018. doi:10.1177/2056305118763366
• Flicker, Sarah, Dave Haans, and Harvey Skinner, 2004, “Ethical Dilemmas in Research on Internet Communities”, Qualitative Health Research , 14(1): 124–134. doi:10.1177/1049732303259842
• Fossheim, Hallvard and Helene Ingierd (eds.), 2016, Internet Research Ethics: , Oslo: Cappelen Damm Akademisk/NOASP. doi:10.17585/noasp.3.1
• Frankel, Mark S. and Sanyin Siang, 1999, “Ethical and Legal Aspects of Human Subjects Research in Cyberspace”, A Report of a Workshop, 10–11 June 1999, Washington, DC: American Association for the Advancement of Science. [ Frankel and Siang 1999 available online ]
• Franzke, Aline Shakti, Anja Bechmann, Michael Zimmer, Charles M. Ess, and the Association of Internet Researchers (AoIR), 2020, Internet Research: Ethical Guidelines 3.0 , AoIR. [ Franzke et al. available online (pdf) ]
• Frauenberger, Christopher, Amy S. Bruckman, Cosmin Munteanu, Melissa Densmore, and Jenny Waycott, 2017, “Research Ethics in HCI: A Town Hall Meeting”, in Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems – CHI EA ’17 , Denver: ACM Press, pp. 1295–1299. doi:10.1145/3027063.3051135
• Gaw, Allan and Michael H. J. Burns, 2011, On Moral Grounds: Lessons from the History of Research Ethics , Westerwood, Glasgow: SA Press.
• [GDPR] General Data Protection Regulation (GDPR), 2016, “Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46”. [ available online ]
• Gilbert, Brendan James, 2009, “Getting to Conscionable: Negotiating Virtual Worlds’ End User License Agreements without Getting Externally Regulated”, Journal of International Commercial Law and Technology , 4(4): 238–251. [ Gilbert 2009 available online ]
• Glickman, Seth W., Sam Galhenage, Lindsay McNair, Zachry Barber, Keyur Patel, Kevin A. Schulman, and John G. McHutchison, 2012, “The Potential Influence of Internet-Based Social Networking on the Conduct of Clinical Research Studies”, Journal of Empirical Research on Human Research Ethics , 7(1): 71–80. doi:10.1525/jer.2012.7.1.71
• Goldfarb, Norman M., 2008, “Age of Consent for Clinical Research”, Journal of Clinical Research Best Practices , 4(6). [ Goldfarb 2008 available online ]
• [HHS] Health and Human Services, 2017, “Excerpts from the January 19, 2017 Revised Common Rule Preamble”. [ HHS 2017 available online ]
• Hill, Kashmir, 2014, “Facebook Added ‘Research’ To User Agreement 4 Months After Emotion Manipulation Study”, Forbes.com. , 30 June 2014. [ Hill 2014 available online ]
• Hoffmann, Anna Lauren, 2016, “Facebook has a New Process for Discussing Ethics. But is It Ethical?” The Guardian , 17 June 2016. [ Hoffmann 2016 available online ]
• Homeland Security Department, 2011, “Submission for Review and Comment: ‘The Menlo Report: Ethical Principles Guiding Information and Communication Technology Research’”, Federal Register: The Daily Journal of the United States Government , FR Doc. 2011-3323, 28 December 2011. [ Homeland Security Department 2011 available online ].
• Hubach, Randolph D., Andrew O’Neil, Mollie Stowe, Zachary Giano, Brenda Curtis, and Celia B. Fisher, forthcoming, “Perceived Confidentiality Risks of Mobile Technology-Based Ecologic Momentary Assessment to Assess High-Risk Behaviors Among Rural Men Who Have Sex with Men”, Archives of Sexual Behavior , first online: 20 February 2020. doi:10.1007/s10508-019-01612-x
• Hudson, James M. and Amy Bruckman, 2004, “‘Go Away’: Participant Objections to Being Studied and the Ethics of Chatroom Research”, The Information Society , 20(2): 127–139. doi:10.1080/01972240490423030
• –––, 2005, “Using Empirical Data to Reason about Internet Research Ethics”, in ECSCW 2005: Proceedings of the Ninth European Conference on Computer-Supported Cooperative Work, 18–22 September 2005, Paris, France , Hans Gellersen, Kjeld Schmidt, Michel Beaudouin-Lafon, and Wendy Mackay (eds.), Berlin/Heidelberg: Springer-Verlag, pp. 287–306. doi:10.1007/1-4020-4023-7_15
• Hunsinger, Jeremy, Lisbeth Klastrup, and Matthew Allen (eds.), 2010, International Handbook of Internet Research , Dordrecht: Springer Netherlands. doi:10.1007/978-1-4020-9789-8
• Illingworth, Nicola, 2001, “The Internet Matters: Exploring the Use of the Internet as a Research Tool”, Sociological Research Online , 6(2): 79–90. doi:10.5153/sro.600 [ Illingworth 2001 available online ]
• International Telecommunications Union, 2019, “New ITU Data Reveal Growing Internet Uptake but a Widening Digital Gender Divide”, ITU Media Centre , [ ITU 2019 available online ]
• Jackman, Molly and Lauri Kanerva, 2016, “Evolving the IRB: Building Robust Review for Industry Research”, Washington and Lee Law Review Online , 72(3): 442–457.
• Jacobson, David, 1999, “Doing Research in Cyberspace”, Field Methods , 11(2): 127–145. doi:10.1177/1525822X9901100204
• Johns, Mark D., Shing-Ling Sarina Chen, and G. Jon Hall (eds.), 2003, Online Social Research: Methods, Issues, and Ethics , New York: Peter Lang.
• Jones, Arnita, 2008, “AHA Statement on IRB’s and Oral History Research”, American Historical Association Activities , 1 February 2008. [ A. Jones 2008 available online ]
• Jones, Steve (ed.), 1999, Doing Internet Research: Critical Issues and Methods for Examining the Net , Thousand Oaks, CA: Sage.
• Kaplan, Andreas M. and Michael Haenlein, 2010, “Users of the World, Unite! The Challenges and Opportunities of Social Media”, Business Horizons , 53(1): 59–68. doi:10.1016/j.bushor.2009.09.003
• Kerry, Cameron F., 2020, “Protecting privacy in an AI-driven world” (AI Governance), 10 February 2020, Center for Technology Innovation, Brookings Institute. [ Kerry 2020 available online ]
• Keyes, Os, 2019, “Counting the Countless: Why data science is a profound threat for queer people”, Real Life , 8 April 2019. [ Keyes 2019 available online ]
• King, Storm A., 1996, “Researching Internet Communities: Proposed Ethical Guidelines for the Reporting of Results”, The Information Society , 12(2): 119–128. doi:10.1080/713856145
• Kitchin, Heather A., 2003, “The Tri-Council Policy Statement and Research in Cyberspace: Research Ethics, the Internet, and Revising a ‘Living Document’”, Journal of Academic Ethics , 1(4): 397–418. doi:10.1023/B:JAET.0000025671.83557.fa
• –––, 2008, Research Ethics and the Internet: Negotiating Canada’s Tri-Council’s Policy , Winnipeg, Manitoba: Fernwood Publishing
• Kramer, Adam D. I., James E. Guillory, and Jeffrey T. Hancock, 2014, “Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks”, Proceedings of the National Academy of Sciences , 111(24): 8788–8790. doi:10.1073/pnas.1320040111
• Kraut, Robert, Judith Olson, Mahzarin Banaji, Amy Bruckman, Jeffrey Cohen, and Mick Couper, 2004, “Psychological Research Online: Report of Board of Scientific Affairs’ Advisory Group on the Conduct of Research on the Internet.”, American Psychologist , 59(2): 105–117. doi:10.1037/0003-066X.59.2.105
• Krogstad, Donald J., Samba Diop, Amadou Diallo, Fawaz Mzayek, Joseph Keating, Ousmane A. Koita, and Yéya T. Touré, 2010, “Informed Consent in International Research: The Rationale for Different Approaches”, The American Journal of Tropical Medicine and Hygiene , 83(4): 743–747. doi:10.4269/ajtmh.2010.10-0014
• Lawson, Danielle, 2004, “Blurring the Boundaries: Ethical Considerations for Online Research Using Synchronous CMC Forums”, in Buchanan 2004: 80–100.
• Leibovici, Didier G., Suchith Anand, Jerry Swan, James Goulding, Gobe Hobona, Lucy Bastin, Sergiusz Pawlowicz, Mike Jackson, and Richard James, 2010, “Workflow Issues for Health Mapping ‘Mashups’ of OGC”, University of Nottingham, CGS Technical Report, 2010 DL1. [ Leibovici et al. 2010 available online ]
• Madejski, Michelle, Maritza Lupe Johnson, and Steven Michael Bellovin, 2011, “The Failure of Online Social Network Privacy Settings”. Columbia Research Report CUCS-010-11, Columbia University. doi:10.7916/D8NG4ZJ1
• Mann, Chris, 2003, “Generating Data Online: Ethical Concerns and Challenges for the C21 Researcher”, in Thorseth 2003: 31–49.
• Markham, Annette N., 1998, Life Online: Researching Real Experience in Virtual Space , Walnut Creek, CA: Altamira Press.
• –––, 2012, “Fabrication as Ethical Practice: Qualitative Inquiry in Ambiguous Internet Contexts”, Information, Communication & Society , 15(3): 334–353. doi:10.1080/1369118X.2011.641993
• Markham, Annette N. and Nancy K. Baym (eds.), 2008, Internet Inquiry: Conversations about Method , Thousand Oaks, CA: Sage Publications.
• Markham, Annette, N. and Elizabeth Buchanan, 2012. : Ethical decision-making and internet research: Version 2.0. recommendations from the AoIR ethics working committee Association of Internet Researchers. [ Markham and Buchanan 2012 available online ]
• Markoff, John, 2010, “In a Video Game, Tackling the Complexities of Protein Folding”, New York Times , 4 August 2010. [ Markoff 2010 available online ]
• McKee, Heidi A. and James E. Porter, 2009, The Ethics of Internet Research: A Rhetorical, Case-based Process , New York: Peter Lang Publishing.
• Mendelson, Cindy, 2007, “Recruiting Participants for Research From Online Communities”, CIN: Computers, Informatics, Nursing , 25(6): 317–323. doi:10.1097/01.NCN.0000299653.13777.51
• Metcalf, Jacob, 2017, “‘The Study Has Been Approved by the IRB’: Gayface AI, Research Hype and the Pervasive Data Ethics Gap”. PERVADE Team: Pervasive Data Ethics for Computational Research, Report. [ Metcalf 2017 available online ]
• Metcalf, Jacob, Emanuel Moss, and danah boyd, 2019, “Owning Ethics: Corporate Logics, Silicon Valley, and the Institutionalization of Ethics”, Social Research: An International Quarterly , 86(2): 449–476.
• Milne, George R. and Mary J. Culnan, 2004, “Strategies for Reducing Online Privacy Risks: Why Consumers Read (or Don’t Read) Online Privacy Notices”, Journal of Interactive Marketing , 18(3): 15–29. doi:10.1002/dir.20009
• Mondschein, Christopher F. and Cosimo Monda, 2018, “The EU’s General Data Protection Regulation (GDPR) in a Research Context”, in Fundamentals of Clinical Data Science , Pieter Kubben, Michel Dumontier, and Andre Dekker (eds.), Cham: Springer International Publishing, pp. 55–71. doi:10.1007/978-3-319-99713-1_5
• Moor, James H., 1985, “What Is Computer Ethics?”, Metaphilosophy , 16(4): 266–275. doi:10.1111/j.1467-9973.1985.tb00173.x
• Alexander, Larry and Michael Moore, 2007, “Deontological Ethics”, The Stanford Encyclopedia of Philosophy (Winter 2007 Edition), Edward N. Zalta (ed.). URL = < https://plato.stanford.edu/archives/win2007/entries/ethics-deontological/ >
• Narayanan, Arvind and Vitaly Shmatikov, 2008, “Robust de-anonymization of Large Sparse Datasets”, Proceedings of the 29 th IEEE Symposium on Security and Privacy, Oakland, CA, May 2008 , IEEE, pp. 111–125. doi:10.1109/SP.2008.33 [ Narayanan and Shmatikov 2008 available online (pdf) ]
• [NCPHSBBR] The National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, 1979, “The Belmont Report: Ethical Principles and Guidelines for the Protection of Human Subjects of Research”, Office for Human Research Protections, Department of Health and Human Services, United States. [ NCPHSBBR 1979 available online ]
• [NESH] The National Committee for Research Ethics in the Social Sciences and the Humanities [Norway], 2006, “Guidelines for Research Ethics in the Social Sciences, Law, and Humanities”, Published September 2006. [ NESH 2006 available online ].
• –––, 2019, “A Guide to Internet Research Ethics”. [ NESH 2019 available online ].
• Nissenbaum, Helen, 2009, Privacy in Context: Technology, Policy, and the Integrity of Social Life , Stanford, CA: Stanford University Press.
• [NSTC] National Science and Technology Council, 2016, “National Privacy Research Strategy”, Office of the President of the United States, June 2016. [ NSTC 2016 available online ]
• Nuremberg Code, 1947 (1996), “The Nuremberg Code”, BMJ , 313. doi:https://doi.org/10.1136/bmj.313.7070.1448
• Ohm, Paul, 2010, “Broken Promises of Privacy: Responding to the Surprising Failure of Anonymization”, UCLA Law Review , 57: 1701–1777.
• Overbeke, Grace, 2008, “Pro-Anorexia Websites: Content, Impact, and Explanations of Popularity”, Mind Matters: The Wesleyan Journal of Psychology , 3: 49–62. [ Overbeke 2008 available online ]
• [PRIM&R] Public Responsibility in Medicine and Research, Bankert, E., Gordon, B., Hurley, E., and Shriver, S. (eds), 2021, Institutional Review Board: Management and Function (third edition). Burlington, MA: Jones and Bartlett.
• Reid, Elizabeth, 1996, “Informed Consent in the Study of On-Line Communities: A Reflection on the Effects of Computer-Mediated Social Research”, The Information Society , 12(2): 169–174. doi:10.1080/713856138
• Reynolds, Ren, and Melissa de Zwart, 2010, “The Duty to ‘Play’: Ethics, EULAs and MMOs”, International Journal of Internet Research Ethics , 3(1): 48–68. [ Reynolds & de Zwart 2010 available online ]
• Ritchie, Donald A., 2003, Doing Oral History: A Practical Guide , New York: Oxford University Press.
• Rosenberg, Åsa, 2010, “Virtual World Research Ethics and the Private/Public Distinction”, International Journal of Internet Research Ethics , 3(1): 23–37.
• Rosser, B. R. Simon, J. Michael Oakes, Joseph Konstan, Simon Hooper, Keith J. Horvath, Gene P. Danilenko, Katherine E. Nygaard, and Derek J. Smolenski, 2010, “Reducing HIV Risk Behavior of Men Who Have Sex with Men through Persuasive Computing: Results of the Menʼs INTernet Study-II”, AIDS , 24(13): 2099–2107. doi:10.1097/QAD.0b013e32833c4ac7
• [SACHRP] Secretary’s Advisory Committee to the Office for Human Research Protections, Unitd States Department of Health & Human Services, 2010, “ SACHRP July 20–21, 2010 Meeting Presentations ”.
• –––, 2013, “ Attachment B: Considerations and Recommendations concerning Internet Research and Human Subjects Research Regulations, with Revisions ”, Final document approved 12–13 March 2013. ( SACHRP 2013 pdf version )
• –––, 2013, “Considerations and Recommendations Concerning Internet Research and Human Subjects Research Regulations, with Revisions”. [ SACHRP 2013 available online ]
• –––, 2015, “ Attachment A: Human Subjects Research Implications of ‘Big Data’ Studies ”, 24 April 2015.
• Samuel, Gabrielle and Elizabeth Buchanan, 2020, “Guest Editorial: Ethical Issues in Social Media Research”, Journal of Empirical Research on Human Research Ethics , 15(1–2): 3–11. doi:10.1177/1556264619901215
• Scholz, Trebor, 2008, “Market Ideology and the Myths of Web 2.0”, First Monday , 13(3): 3 March 2008. [ Scholz 2008 available online ]
• Schwartz, Paul M. and Daniel J. Solove, 2011, “The PII Problem: Privacy and a New Concept of Personally Identifiable Information”, New York University Law Review , 86(6): 1814–1893.
• Seaboldt, James A. and Randy Kuiper, 1997, “Comparison of Information Obtained from a Usenet Newsgroup and from Drug Information Centers”, American Journal of Health-System Pharmacy , 54(15): 1732–1735. doi:10.1093/ajhp/54.15.1732
• Sharf, Barbara F., 1997, “Communicating Breast Cancer On-Line: Support and Empowerment on the Internet”, Women & Health , 26(1): 65–84. doi:10.1300/J013v26n01_05
• Sieber, Joan E., 1992, Planning Ethically Responsible Research: A Guide for Students and Internal Review Boards , Thousand Oaks, CA: Sage.
• –––, 2015, Planning Ethically Responsible Research: A Guide for Students and Internal Review Boards , second edition, Thousand Oaks, CA: Sage.
• Simmhan, Yogesh, Roger Barga, Catharine van Ingen, Ed Lazowska, and Alex Szalay, 2008, “On Building Scientific Workflow Systems for Data Management in the Cloud”, in 2008 IEEE Fourth International Conference on EScience , Indianapolis, IN: IEEE, pp. 434–435. doi:10.1109/eScience.2008.150
• Simmhan, Yogesh, Catharine van Ingen, Girish Subramanian, and Jie Li, 2009, “Bridging the Gap between the Cloud and an EScience Application Platform”. Microsoft Research Tech Report MSR-TR-2009-2021. [ Simmhan et al. 2009 available online ]
• Skloot, Rebecca, 2010, The Immortal Life of Henrietta Lacks , New York: Crown Publishers.
• Sloan, Luke, Curtis Jessop, Tarek Al Baghal, and Matthew Williams, 2020, “Linking Survey and Twitter Data: Informed Consent, Disclosure, Security, and Archiving”, Journal of Empirical Research on Human Research Ethics, 15(1–2): 63–76.
• Smith, Michael A. and Brant Leigh, 1997, “Virtual Subjects: Using the Internet as an Alternative Source of Subjects and Research Environment”, Behavior Research Methods, Instruments, & Computers , 29(4): 496–505. doi:10.3758/BF03210601
• Spriggs, Merle, 2010, A Handbook for Human Research Ethics Committees and Researchers: Understanding Consent in Research Involving Children: The Ethical Issues , Melbourne: The University of Melbourne/Murdoch Childrens Research Institute/The Royal Children’s Hospital Melbourne, version 4. Spriggs 2010 available online ]
• Stahl, Bernd Carsten and David Wright, 2018, “Ethics and Privacy in AI and Big Data: Implementing Responsible Research and Innovation”, IEEE Security & Privacy , 16(3): 26–33. doi:10.1109/MSP.2018.2701164
• Sveningsson, Malin, 2004, “Ethics in Internet Ethnography” in Buchanan 2004: 45–61.
• Sweeney, Latanya, 2002, “K-Anonymity: A Model for Protecting Privacy”, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems , 10(5): 557–570. doi:10.1142/S0218488502001648
• Thomas, Jim, 2004, “Reexamining the Ethics of Internet research: Facing the Challenge of Overzealous Oversight”, in Johns, Chen, and Hall 2004: 187–201.
• Thorseth, May (ed.), 2003, Applied Ethics in Internet Research (Programme for Applied Ethics Publication Series No. 1), Trondheim, Norway: NTNU University Press.
• Tsai, Janice, Lorrie Faith Cranor, Alessandro Acquisti, and Christina M. Fong, 2006, “What’s It To You? A Survey of Online Privacy Concerns and Risks”. NET Institute Working Paper No. 06–29. doi:10.2139/ssrn.941708
• Turkle, Sherry,1997, Life on the Screen: Identity in the Age of the Internet , New York: Touchstone.
• Van Heerden, Alastair, Doug Wassenaar, Zaynab Essack, Khanya Vilakazi, and Brandon A. Kohrt, 2020, “In-Home Passive Sensor Data Collection and Its Implications for Social Media Research: Perspectives of Community Women in Rural South Africa”, Journal of Empirical Research on Human Research Ethics , 15(1–2): 97–107. doi:10.1177/1556264619881334
• Vitak, Jessica, Nicholas Proferes, Katie Shilton, and Zahra Ashktorab, 2017, “Ethics Regulation in Social Computing Research: Examining the Role of Institutional Review Boards”, Journal of Empirical Research on Human Research Ethics , 12(5): 372–382. doi:10.1177/1556264617725200
• Walstrom, Mary K., 2004, “Ethics and Engagement in Communication Scholarship: Analyzing Public, Online Support Groups as Researcher/Participant-Experiencer”, in Buchanan 2004: 174–202.
• Walther, Joseph B., 2002, “Research Ethics in Internet-Enabled Research: Human Subjects Issues and Methodological Myopia”, Ethics and Information Technology , 4(3): 205–216. doi:10.1023/A:1021368426115
• White, Michele, 2002, “Representations or People?”, Ethics and Information Technology , 4(3): 249–266. doi:10.1023/A:1021376727933
• World Medical Association, 1964/2008, “Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects”. Adopted by the 18 th World Medical Assembly. Amended 1975, 1983, 1989, 1996, 2000, 2002, 2004, 2008. [ Declaration of Helsinki available online ]
• Wright, David R., 2006, “Research Ethics and Computer Science: An Unconsummated Marriage”, in Proceedings of the 24th Annual Conference on Design of Communication: SIGDOC ’06 , Myrtle Beach, SC: ACM Press, pp. 196–201. doi:10.1145/1166324.1166369
• Zimmer, Michael T., 2010, “‘But the Data Is Already Public’: On the Ethics of Research in Facebook”, Ethics and Information Technology , 12(4): 313–325. doi:10.1007/s10676-010-9227-5
• –––, 2016, “OkCupid Study Reveals the Perils of Big-Data Science”, Wired.com , 14 May 2016. [ Zimmer 2016 available online ]
• Zimmer, Michael and Edward Chapman, 2020, “Ethical Review Boards and Pervasive Data Research: Gaps and Opportunities”, Paper presented at AoIR 2020: The 21st Annual Conference of the Association of Internet Researchers. [ Zimmer and Chapman 2020 extended abstract available online (pdf) ]
• Zimmer, Michael T. and Katharina Kinder-Kurlanda (eds.), 2017, Internet Research Ethics for the Social Age: New Challenges, Cases, and Contexts , New York: Peter Lang Publishing.

How to cite this entry . Preview the PDF version of this entry at the Friends of the SEP Society . Look up topics and thinkers related to this entry at the Internet Philosophy Ontology Project (InPhO). Enhanced bibliography for this entry at PhilPapers , with links to its database.

Other Internet Resources

• Allan, Rob, 2012, “ Cloud and Web 2.0 Services for Supporting Research ”.
• Chen, Xiaoyu, Gary Wills, Lester Gilbert, and David Bacigalupo, 2010, “Using Cloud for Research: A Technical Review”, JISC Final Report for the TeciRes project. [ X. Chen et al. 2010 available online ]
• Dittrich, David and Erin Kenneally, 2012, The Menlo Report: Ethical Principles Guiding Information and Communication Technology Research , Homeland Security, United States Government.
• Hoofnagle, Chris Jay and Jennifer King, 2008, “What Californians Understand About Privacy Online”, Research Report from Samuelson Law Technology & Public Policy Clinic, UC Berkeley Law: Berkeley, CA. doi:10.2139/ssrn.1262130 [ Hoofnagle and King 2008 available online ]
• [NHRPAC] National Human Subjects Protection Advisory Committee, 2002, “ Recommendations on Public Use Data Files ”.
• [NIH] National Institutes of Health, 2010, “ Guide for Identifying and Handling Sensitive Information at the NIH ”
• –––, 2021, “Certificates of Confidentiality (CoC)”, National Institutes of Health [ NIH 2021 available online ]
• Rudder, Christian, 2014, “We Experiment on Humans!”, OkTrends, 28 July 2014. [ Rudder 2014 available online ]
• Sparks, Joel, 2002, Timeline of Laws Related to the Protection of Human Subjects , National Institutes of Health.
• Stone, Brad, 2009, “Facebook Rolls Out New Privacy Settings”, New York Times , 9 December 2009. [ Stone 2009 available online ]
• U.K. Data Archive, 2011, “Managing and Sharing Data: Best Practices for Researchers”. [ UK Data Archive available online ]
• Williams, George, 2010, “ The Ethics of Amazon’s Mechanical Turk ”, ProfHacker Blog, The Chronicle of Higher Education, 1 March 2010.
• Zimmer, Michael, 2009, “Facebook’s Privacy Upgrade is a Downgrade for User Privacy”, MichaelZimmer.Org. [ Zimmer 2009 available online ]

United States

• 45 C.F.R. § 46, “Protection of Human Subjects”, and in particular the Common Rule, 45 C.F.R. 46 Subpart A
• 45 C.F.R. § 164.514, “Other requirements relating to uses and disclosures of protected health information”
• [OHRP] U.S Department of Health and Human Services, 2008, “Office for Human Research Protections”, [ Office for Human Research Protection ]
• The Belmont Report: Ethical Principles and Guidelines for the Protection of Human Subjects of Research
• U.S. Department of Health and Human Services: Can an electronic signature be used to document consent on parental permission?
• U.S. Department of Health and Human Services: What are the basic elements of informed consent?
• Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans , Canada
• European Parliament and Council of European Union (2016) Regulation (EU) 2016/679. ... Data Protection Act 2018, c. 12 [ Data Protection Act 2018 available online ]
• American Counseling Association: Ethics and Professional Standards , 2014 revision
• American Psychological Association: Advisory Group on Conduction Research on the Internet
• Association of Internet Researchers Ethics Guidelines
• Journal of Empirical Research on Human Research Ethics
• Journal of Medical Internet Research
• MethodSpace , SAGE Publishing hosted.
• Research Ethics Blog , run by Nancy Walton.
• Research Ethics Guidelines for Internet Research (pdf) , The (Norwegian) National Committee for Research Ethics in the Social Sciences and the Humanities, 2003.
• Forrester’s Global Data Protection and Privacy Heatmap
• Council of European Social Science Data Archives (CESSDA)
• Foundation Texts of the learning module, Current Issues in Research Ethics: Privacy and Confidentiality , Joyce Plaza and Ruth Fischbach, Columbia University, New York: Columbia Center for New Media Teaching & Learning.
• Ethical and Legal Aspects of Human Subjects Research in Cyberspace , American Association for the Advancement of Science.

ethics, biomedical: clinical research | ethics: deontological | informed consent | privacy

Copyright © 2021 by Elizabeth A. Buchanan < buchanane @ uwstout . edu > Michael Zimmer < michael . zimmer @ marquette . edu >

• Accessibility

Support SEP

Mirror sites.

View this site from another server:

• Info about mirror sites

The Stanford Encyclopedia of Philosophy is copyright © 2023 by The Metaphysics Research Lab , Department of Philosophy, Stanford University

Library of Congress Catalog Data: ISSN 1095-5054

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

34 Using the Internet for Research

What you’ll learn to do: d escribe strategies for successful internet research.

The first step to an effective internet search is being familiar with the terms you are searching for. You search term should be as concise as possible, while still covering the area you would like to find. —Eric Popkoff, professor

By the end of this section, you’ll be able to describe good practices for reading using technology and explain how to research using technology. You’ll also describe techniques for keeping your personal information safe in an online context and explore additional resources for learning online.

Online Reading Strategies

Learning outcomes.

• Describe effective strategies for reading online

In an online educational environment, you’re probably going to do more reading than listening. You may do some of your reading in printed form—say, an assigned novel or textbook—but some of it might also be online in the form of a web page. Reading online isn’t the same as reading in print, so you should practice some strategies that will improve your online reading comprehension and speed. Some of the tactics you learn about here will help you with any kind of reading you might do, not just the stuff that’s online.

Print vs. Online

So what do we mean when we say that reading print is different from reading online?

Evaluate the Source for Credibility

First, when you read something—let’s say, a book—that’s been printed by a reputable publishing house, you can assume that the work is authoritative. The author had to be vetted by a publishing house and multiple editors, right? But when you read something online, it might have been written or posted by anybody. This means that you have to seriously evaluate the authority of the information you’re reading. Pay attention to who was writing what you’re reading—can you identify the author? What are his or her credentials?

Online Reading is Interactive

Second, in the print world, texts may include pictures, graphics, or other visual elements to supplement the author’s writing. But in the digital realm, this supplementary material might also include hyperlinks, audio, and video as well. This material will fundamentally change the reading experience for you because online reading can be interactive in a way that a print book can’t be. An online environment allows you to work and play with content rather than passively absorbing it.

Reading Online Can Lead You to Unexpected Places

Finally, when you read in print, you generally read sequentially, from the first word to the last. Maybe you’ll flip to an index or refer to a footnote, but otherwise the way you read is fairly consistent and straightforward. Online, however, you can be led quickly into an entirely new area of reading by clicking on links or related content. Have you ever been studying for class and fallen down a Wikipedia rabbit hole while looking for unfamiliar terms? You might have started by investigating the French Revolution, but half an hour later you find yourself reading about the experimental jazz scene in 1970s New York. You can’t really do that with a book.

Reading Comprehension: Why, What, How?

Now that you’ve heard about how reading online differs from reading print, you should know that these differences have some really practical consequences for reading comprehension. Improving your online reading comprehension will save you time and frustration when you work on your assignments. You’ll be able to understand your course subject matter better, and your performance on your quizzes and exams will improve.

Consider the why, what, and how of reading comprehension:

• Why  am I being asked to read this passage? In other words, what are the instructions my professor has given me?
• What  am I supposed to get out of this passage? That is, what are the main concerns, questions, and points of the text? What do you need to remember for class?
• How  will I remember what I just read? In most cases, this means taking notes and defining key terms.

When you keep the why, what and how of reading comprehension in the forefront of your mind while reading, your understanding of the material will improve drastically. It will only take a few minutes, but it will not only help you remember what you’ve read, but also structure any notes that you might want to take.

Explore a Web Page

Let’s put this information to use with a short exercise. Imagine that your instructor has asked you to create an argument either for or against the institution of the death penalty in California. She has pointed you to the website www.deathpenalty.org to get started. What terms or headlines stick out at you so you can begin crafting your argument? Consider the following headlines of articles from the website. Which articles seem like they might work best for helping you get started?

• “Federal Judge Says CA Death Penalty Unconstitutional”: Great! This article will have a legal argument from a federal judge—a fantastic place to get talking points for your own argument.
• “The Death Penalty Failure They’re Trying To Hide”: Good instincts—this article may give you a great point of counterattack if your argument is against the death penalty.
• “Infographic: The First Time We Ended the Death Penalty”: Yes! This will give you a historical precedent you can point to in your argument.
• “Polls Show Preference for Death Penalty Alternatives”: Well done—what’s more convincing than numbers, especially when it comes to the will of the American people?
• “Former Florida Warden Haunted By Botched Execution”: Yes—a great rhetorical tactic is to use an anecdote from the life of a person with experience with the issues you’re talking about, and this article sounds like it might be very moving. After all, it was convincing enough to change this man’s mind about the death penalty—maybe it would sway your audience as well.
• “DPF Appoints New Director of Community Outreach and Education”: Hmm, this article doesn’t seem to be the best option for your argument because it’s not directly related to your argument. Let’s skip or skim this one!
• “How to Stop a Heart”: This is another good testimony from someone affected by the death penalty, but it’s in the form of a blog post, so there’s probably better evidence out there. Maybe come back to it if you don’t have everything you need.
• “Michael Millman accepts ‘Lifetime Achievement Award’”: This article doesn’t really pertain to your assignment, so it doesn’t seem like the best possible choice. Keep looking!

Tips for Reading Online

Reading online can be challenging, but here are a few tips to help:

Getting Distracted While Reading Online

When you read online, the hyperlinks, images, audio, and video interactivity embedded in the text can be a really tempting distraction. Try reading a passage straight through at least once without clicking on any of the hyperlinks or participating in any of the interactive opportunities. First, get a basic feel for the passage, then read it with the interactive components to augment your reading.

Reading Assignments on Your Phone

It’s best not to read your assignments from the small screen of a smartphone. It’s too easy to miss words and meanings when the reading process itself is challenging.

Increasing My Reading Speed

Reading quickly and efficiently will leave you more time to study, and improve your performance in your course.

To read more quickly and efficiently online, try most of all to avoid distractions like ads, pop-ups, or hyperlinks that will lead you away from your assignment. Another tactic you can try is to scan the page before actually reading, focusing on keywords and phrases rather than every single word. This is the same technique you just tried out in the death penalty exercise we went through. It will not only help you to read faster, it’ll also give you a sense of the text’s main ideas.

Research Using Technology

• Explain how to research using technology

Research Using the Internet

Using the Internet when researching for a class assignment is an essential skill for any successful student. The research process should not be frustrating or difficult when you follow the steps of the research process and evaluate your sources so you only use credible and reliable information. Before we discuss the process for researching using the Internet, it is important to think about what research is. This short video will provide you with a basic understanding of the research process.

Research Process

The research process includes a range of steps to ensure you are successful in finding the information you need using the Internet. The first step is to define your topic. While this statement seems straightforward, it is important to think about what you are actually researching for an assignment. A professor may give you a general topic as a starting point for your research. If you use the general topic when conducting research on the Internet, you could receive millions of results. Instead, think about what you really want to learn from your research and narrow the focus of your topic to something that is manageable.

If you find that you are having trouble understanding your topic or even narrowing the focus of your topic, find background information. This background information can be from a range of sources. Think about Wikipedia , which is ostensibly an encyclopedia. While for most topics, Wikipedia can provide you with the background you need to better understand your topic, it is always important to evaluate the information you find on the Internet for accuracy.

Once you define your topic and have a better understanding of the issues related to your topic based on your background research, you can develop a research question to guide your research using the Internet. Your research question should provide enough information that anyone can understand the purpose of your research. Once you have a research question, you can use your research question to develop a research strategy. Your research strategy will comprise the main concepts of your research question that can be used as your keywords and search terms.

Now that you have a research strategy, you will need to choose a proper search tool. This tool can be a search engine like Google or maybe a library database that is available to you through your institution. As you think about which search tool to select, it is important to think about what type of information you need. You can use a general search engine like Google to find a range of information, but may need to use a library database to find scholarly journal articles.

Finally, you will perform your search and evaluate your results. As you look at your search results, consider if the information you are finding answers your research question and is from a reliable source.

Search Strategy

Now that you have an understanding of your topic, have defined what you will be researching, and have utilized background information to develop a research question, it is time to develop a search strategy. If your general topic is “social media privacy,” it is helpful to focus your research question. You can focus your search on something like, what action should social networking sites like Instagram and Facebook take to protect users’ personal information and privacy?

From the above research question, you can develop your research strategy by focusing on the main concepts in your research question.

• Personal Information

Using these key concepts from your research question, you can develop your search strategy.

Build a Strong Search Strategy

Learn how to build a strong search strategy through this video below.

Tips and Tricks for Internet Searching

In this search strategy, you see there are a number of different things happening: “social networks” AND user* AND “personal information” AND privacy.

You see that social networks and personal information are in quotes. These quotations are called phrase searching . By placing quotation marks around a phrase, you are telling the search tool to look for those words together. In this case, the search tool will look for the words social network together and the words personal information. This ensures more accurate results when you search.

You will also notice an asterisk after the search term user . This asterisk is called truncation and will tell the database to search for not only user but other terms that start with user like users.

You will notice the word AND capitalized between each search term/phrase. This capitalization is a Boolean operator and it tells the search tool to connect my search terms together and look for a source that includes all the terms. You can broaden your search by using the OR Boolean operator to search for Twitter or Facebook. And the NOT Boolean operator to search for Twitter NOT Facebook.

You can find additional tips for searching the Internet here .

Evaluating Information to Determine Credibility

Once you find information through your search strategy, it is important to evaluate the information you are using to determine if it is credible and reliable. You can do this by using the CRAAP test. CRAAP stands for

• Accuracy, and

You can learn more about the CRAAP test as a tool to evaluate Internet sources here .

SIFT Method

You can also evaluate information, particularly information found on social media, using the SIFT method. View the following video to learn more about SIFT.

Attributing Your Sources in Your Writing

When using information from a source such as a website, journal article, magazine article, newspaper article, or books and eBooks, it is important that you attribute these ideas in your academic writing. The Purdue Online Writing Lab (Purdue OWL) is a great source of information on how to cite your sources.

Safety and Personal Information on the Internet

• Describe techniques for keeping your personal information safe in an online context

More than ever before, it’s critical to keep your personal information safe on the Internet. It seems like every day there is another news story about a new database breach or identity theft scam. But in a world where we’re all connected almost constantly, how do you even know where to begin to protect your data and online identity?

As an example, let’s look at Eliana, a freshman at Mountain Brush Community College. Eliana has a Chromebook that she uses both for her schoolwork as well as her personal Internet use. She also has a Google-branded smartphone, and on both of these devices she uses the apps that came with them, like Gmail, Google Docs, and Google Maps. Google keeps Eliana’s information very secure. However, she doesn’t have much privacy, at least when it comes to Google—they keep an astounding amount of information about Eliana. She has to decide whether she trusts Google to know so much about her or not.

Unfortunately, both security and privacy often come at the cost of some amount of convenience. It’s up to you to decide where the right balance is for you, but in order to make that decision, you need to understand the tradeoffs.

Interestingly, in today’s world, security is often easier to achieve than privacy. For one thing, we’re all used to some of the steps we have to take to keep our information secure, and it’s actually in the best interests of the big tech companies like Google, Apple, Microsoft, Amazon, and Facebook to help us keep our information secure. The same is not true for privacy, as we’ll discuss later.

Passwords and Password Managers

The first step to achieving better online security is the one that we’re all familiar with—maintaining good, separate passwords for all of our online accounts. While it’s much easier to use a few simple, easy-to-type passwords for most of your accounts (and many people still do!), this is a great example of sacrificing security for convenience. The risk is that if someone obtains your email address and password for one account (for example, through a data breach at a company you do business with), they have your login credentials for many of your accounts and can start doing real damage.

Fortunately, password managers are a great tool for maintaining quite a bit of convenience in this scenario while achieving high levels of security. A password manager is an app that runs on all of your devices (computer, phone, tablet, etc.) and stores your passwords for all your online accounts. You only have to remember a single password: the one that unlocks your password manager. This app makes it easy to create super-secure unique passwords for your online accounts because you never have to remember them! Some popular password managers are 1Password , LastPass , and Bitwarden .

Two-Factor Authentication (2FA)

Two-factor authentication (2FA) is an extra layer of security on top of your existing password. To gain access to a site, you must enter your password and then provide a second piece of information—often a code that is texted to your phone number. This increases the likelihood that you are who you say you are, and helps to prevent unauthorized access to your account. Unfortunately, this comes at the cost of convenience—it can be annoying to have to enter two pieces of information every time you log on to a site! A good compromise is to use 2FA on your most important accounts, where the most damage could be done if someone gains access to them—for example, bank accounts, your school account, and your email account.

That last one, your email account, is more important than you might think at first. If someone gains access to your email account, they can immediately change your password to lock you out, and then begin to go through all your online accounts, resetting your passwords to gain access to all those accounts. So protecting your email account should benext to protecting your bank accounts in terms of priority.

Security Updates

As hackers find exploits in software and operating systems that run on our phones and computers, security updates on our devices try to block these exploits. However, it’s up to you to make sure that you keep the software on your phone and computer up to date so that you get the latest security patches. The easiest way to do this is to set your devices to download and install security updates automatically.

Antivirus/Anti-malware

Another effective way to block unauthorized access of your personal data is to run antivirus/anti-malware software on your computer. Bitdefender , Malwarebytes , and McAfee Total Protection are some common software programs you can check out.

Ad Blockers

An ad blocker is an extension you run in your web browser that not only keeps you from being inundated with ads, but can help prevent your computer from becoming infected by malware. Some browsers now block ads without you having to install anything (e.g., Brave). For other browsers, common ad blocker extensions are AdBlock Plus and uBlock Origin .

Finally, you should take steps to protect your data from yourself! We don’t mean, of course, that you are likely to steal your own data. Rather, data loss is a common occurrence that, while not nefarious in nature, can still be very problematic. We tend to rely on cloud backups for more and more of our data these days, but it’s worth giving some thought to what happens to your data if your account is closed, or the company goes away, or even if you just exceed your storage limits and don’t realize it before your data starts being deleted. For pictures and documents that we store in the cloud and on our computers and phones, it’s good to have a backup stored somewhere safe—for example, on an external hard drive.

Remember Eliana from our discussion above? She was the freshman who uses a Chromebook and a Google-branded smartphone, and on both of these devices she uses the apps that came with them, like Gmail, Google Docs, and Google Maps. While Google keeps Eliana’s information very secure, we noted that she doesn’t have much privacy, at least when it comes to Google. Google knows her name, email address, home address, birthday, gender, and phone number. They know what she looks like, what she sounds like, who her friends are, how much she talks to them, and what she talks about with them. They know what her interests are, what she searches for online, what she buys online, where she goes, what stores and restaurants she likes to visit, how much time she spends there, and how fast she drives. If Eliana wears a Fitbit to track her steps, Google also knows her weight, height, age, fitness goals, and how many calories she burns in a day.

To look through some of the data Google has stored about you specifically, you can visit https://takeout.google.com . Check all of the boxes that you’re interested in, and then click the button to export your data. It can take hours or days for Google to assemble the download for you, but they will email you when it’s ready, and you can poke through all your personal information that Google has stored. To limit the amount of data that Google collects on you, and to delete saved data, you can visit https://myaccount.google.com/activitycontrols .

The following image is from Google Takeout, and shows all the different categories of data that Google collects about you:

What if Eliana was in the Apple ecosystem as opposed to Google’s—would she be better off in terms of privacy? The answer depends in part on whether or not she’s using all of the same Google apps—Gmail, Google Maps, Google Search, etc.—on her Apple devices that she was on her Google devices. If she is using Google apps, then her situation is very similar. If she has opted to avoid all Google apps, then her level of privacy has improved as Apple is not sharing her data with advertisers. Remember that a key part of Google’s business model is creating a profile of you—all your interests, online purchases, web searches, etc., and using that data to deliver targeted ads to you. Apple’s business model is different. Apple charges you higher prices for their products and services rather than delivering ads to you. Because of this difference in business models, a study in 2021 found that Google collects around twenty times more handset data than Apple [1] .

However, it’s worth noting that Apple is still tracking Eliana in many of the same ways that Google does and storing her information on their servers for their own uses. So in the end, she would still need to decide to what extent she is willing to trust a large tech company with all of her personal information.

What can you do to improve your privacy? After all, almost all of us use computers and smartphones on a daily basis, and some amount of data capturing and tracking is all but unavoidable in order to use the services that we need in everyday life. However, there are some steps you can take to begin to improve your privacy without losing too much in terms of convenience.

Text Messaging

Signal and Telegram are two good privacy-focused messaging services you can use instead of the apps that come with your smartphone, and both of them are free. On an Android phone, you can even set Signal to be your default text messaging app, and it will let you communicate with your non-Signal-using friends in addition to other Signal users (unfortunately, this isn’t possible on Apple devices due to limitations in the iOS).

Web Searching

Google is the undisputed king of web searching. However, privacy-focused alternatives are starting to appear. One of the best is DuckDuckGo . Unlike Google, it doesn’t collect or share any kind of identifiable personal information. DuckDuckGo can be used in a web browser on your computer, and is also available as an app for your smartphone.

Internet Browsing

While Google Chrome has the highest market share of all browsers, other privacy-focused alternatives exist that are arguably just as good. Two popular ones are Brave and Firefox . Brave is actually built on the underlying, open-source code that Chrome is built from, which means that the browser plug-ins you use with Chrome will also work with Brave. Firefox is not built on the same technology, but has a wide range of plug-ins available as well.

Aside from privacy concerns, Gmail is a great email service—it’s easy to use, it works on almost any device, and it’s free. But again, it’s only free because Google’s business model is to sell ads rather than charging their customers. If you’re interested in improving your privacy, there are a number of email providers that you can choose from that offer private email, but the tradeoff is that they cost a few dollars per month. Popular ones include ProtonMail , FastMail , and Tutanota .

Using the Internet for Lifelong Learning

• Explore additional resources for learning using the Internet

Lifelong Learning Using the Internet

A Pew Research Center (2016) survey found that “73% of adults consider themselves lifelong learners.” This study also found that “74% of adults are what we call personal learners—that is, they have participated in at least one of a number of possible activities in the past 12 months to advance their knowledge about something that personally interests them” (Pew Research Center, 2016). This same study found that “63% of those who are working (or 36% of all adults) are what we call professional learners—that is, they have taken a course or gotten additional training in the past 12 months to improve their job skills or expertise connected to career advancement.” [2]

Whether for personal or professional development, it is now essential for everyone to continue learning throughout their life to stay on top of technological changes and innovations in society. While the Internet has been a gamechanger for how information is shared, it also provides those interested in lifelong learning with a number of options to stay on top of any topic.

As a lifelong learner, it is important to set your personal and professional objectives for learning, determine the best platform for learning, and evaluate your lifelong learning to ensure you are meeting your objectives. Like any process, you will likely need to refine your objectives as you advance personally and professionally.

Basic Lifelong Learning on the Internet

As you think about your own lifelong learning and how the Internet can facilitate the process, it is important to consider the range of platforms that are available to you and consider them for the content they provide.

For anyone who has a problem around the home, say a leaky faucet, YouTube is an excellent source of informative videos through this platform provider. The videos are often associated with user-created content from a range of sources, but it is important to know that companies and organizations often host how-to videos on this platform to reach a wide audience. These videos offer a quick and focused opportunity for lifelong learning.

In addition to YouTube videos, there are other platforms that offer skills-based lifelong learning. These platforms include WikiHow , which provides how-to guides on a range of topics.

MOOC Platforms

The range of available platforms for lifelong learning on the Internet continues to grow, but includes options based on your personal and professional interests and needs. Here is a list of massive open online course (MOOC) providers that can meet lifelong learning needs. While the courses offered as MOOCs should be open and freely available, some providers do charge fees for transcripts or certificates to indicate you have completed a course or program. Of course, this list will change but it offers an excellent starting point for anyone interested in learning a new skill or expanding their knowledge.

• Great Courses
• Khan Academy

Lifelong Learning through Synchronous Events

Whether for personal or professional growth, synchronous webinars hosted by professional organizations and other associations offer an excellent option for lifelong learning. In the professional space, webinars are often hosted by professional organizations and associations focused on providing professionals in a field with training to meet the current needs of their members. For example, the American Nurses Association serves as a professional association for registered nurses. This association, like those in other industries, offers professional development opportunities to their members. You can see on the American Nurses Association website that they offer a range of workshops, webinars, and continuing education courses.

While professional organizations and associations offer a range of lifelong learning opportunities, there are also opportunities for lifelong learning for personal growth. Just as the American Nurses Association offers a range of workshops, webinars, and continuing education courses, you can find lifelong learning opportunities from a range of organizations. For example,  the Smithsonian Institution offers a range of online events and has expanded their offerings through their Smithsonian Associates Streaming platform that includes lectures and tours. For those interested in continuing their personal education, it is a matter of finding the right organization or institution.

Regardless of your lifelong learning goals, the Internet will continue to expand access to information, making it easier for anyone to grow personally and professionally.

Boolean operator: terms such as AND, OR, and NOT that can be inserted to categorically focus an online search

interactive: the unique quality of online texts that allows a reader to move in a non-linear fashion to hyperlinked material and mixed-media resources

phrase searching: the online research technique that involves placing quotations around a phrase, which tells a search tool to look for those words together

privacy: the degree of control we have over who sees our online data and identity

security: the degree to which we protect our online data and identity

truncation: the online research technique that places an asterisk after a term to find terms that include and extend from the original term

vetted: a term describing an authoritative text that has been carefully reviewed, edited, and most likely peer-reviewed by qualified scholars

• Leith, Douglas J. "Mobile Handset Privacy: Measuring The Data iOS and Android Send to Apple And Google."  Trinity College Dublin, Ireland , 25 March 2021, https://www.scss.tcd.ie/doug.leith/apple_google.pdf ↵
• Horrigan, John B. "Lifelong Learning and Technology."  Pew Research Center , 2016, http://www.pewresearch.org/internet/2016/03/22/lifelong-learning-and-technology/. ↵

a term describing an authoritative text that has been carefully reviewed, edited, and most likely peer-reviewed by qualified scholars

the unique quality of online texts that allows a reader to move in a non-linear fashion to hyperlinked material and mixed-media resources

the online research technique that involves placing quotations around a phrase, which tells a search tool to look for those words together

the online research technique that places an asterisk after a term to find terms that include and extend from the original term

terms such as AND, OR, and NOT that can be inserted to categorically focus an online search

the degree to which we protect our online data and identity

the degree of control we have over who sees our online data and identity

Using the Internet for Research Copyright © 2023 by April Ring is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

Share This Book

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Use of Internet for Research and Educational Activities by Research Scholars : A Study of D.S.B. Campus of Kumaun University - Nainital

Related Papers

ATBU Journal of Science, Technology and Education

yakubu Bala

The paper was conducted in order to ascertain how the use of internet can be a positive catalyst to learning and research to students of library and information sciences ATBU, Bauchi. Cross sectional survey design was used using structured questionnaire and administered for data collection. The questionnaire was designed, keeping in view the objectives of the study. The target populations for the study were male and female students of library and information sciences. Random sampling technique was used for questionnaire distribution. One hundred and thirty (130) questionnaires were administered to the respondents. Some were dully returned. The data obtained were analyzed using Microsoft excel (spreadsheet programme). The study reveals among other things that 80 (81.63%) students admitted that internet improves their academic pursuit and are satisfied with the use of internet and the kind of information available on the internet. The study also reveals that, majority of the students ...

Abdulkadir Aliyu

The Ijes The Ijes

Murugan Mani

The main purpose of the study awareness and use of internet resources of information The researcher prepared questionnaire with 12 questions to be answered by the respondents about the level and purpose of using Internet resources of the 100 questionnaires distributed 81 were taken for the analysis 54.32% the respondents belong to Female category, 46.91% of the respondents belong to the age group of 21-25, 45.67% of respondents belong to the category of Ph.D. scholars, 38.27% of respondents belong to the rural area, 87.65% respondents belong to the awareness of 'Yes', 48.16% of respondents belong to the Access at Library, 50.61% of respondents belong to the More than two hours time spent, 44.44% of respondents belong to the purpose of For the project, 27.16% of respondents belong to the E-books, 53.08% of respondents belong to Very good, 44.44% of respondents belong to update knowledge, 51.85% of respondents belong to the Fully satisfied.

Suchetan Sah

The article reveals about the use of the Internet in libraries by faculty members and research scholars at GB Pant Library, DSB Campus, Kumaun University Nainital. A total of 150 questionnaires were distributed, 40 to faculty and 110 to research scholars. A total of 140 valid questionnaires were collected from faculty members (34) and research scholars (106). The response rate was 86.67%. The study reveals that research scholars use internet daily and their favorite place for accessing it is GB Pant Library. Research and education are the top reasons for accessing internet. However open access journals are most preferred resources. But most respondents face downloading problem due to low speed.

Fasa Rachael Aladeniyi , Joseph Fasae

The study aimed at examining the use of Internet by students of faculty of science in two Nigerian Universities. The population for the study was 1200 and sample size of 240 was used. Two hundred and forty copies of questionnaire were administered to the respondents. This produced a return rate of 87.5%. The data collected was analysed using frequency and simple percentage and presented using tables. Among the findings of the study are that; most of the respondents use the Internet 2 – 3 times in a week and nearly all the respondents use the Internet facilities located outsides the campus. Also, slow access speed (96%) was the leading problem and almost all the respondents believed that Internet have increased their access to current information, ease research process and improved their professional competency. The study concluded that a majority of the students of faculty of science in Nigerian University are satisfied with the use of the Internet. Recommendations were made for further studies.

International Journal of Knowledge Content Development and Technology

DAUDA JOSHUA

Library Philosophy and Practice

Muhammad Aqil

Resumen: This paper studies the Internet as an information source for research scholars and PG students of the Science faculty, Aligarh Muslim University (AMU), India. A structured questionnaire is distributed to the research scholars and the PG students of Science Faculty, AMU. The study shows Internet-based information sources are more useful than print sources. This study also indicates the Internet is most satisfactory source of information. There are many related studies on the use of Internet but this study is the first of its kind ...

IOSR Journal of Humanities and Social Science

Abdulfatah Abdulrahim

FALANA TAYE FREDRICK

The advent of the internet has revolutionized the life of the global community in all ways. Its usefulness seems to manifest greatly in the academic circles, where students use the internet for information research and relaxation. The internet is the most popular (IT) Information Technology resource which has also been embraced by the academic community in Nigeria. It uses has been to solve scarcity of textbooks, collaborate with international colleagues and to update knowledge. The internet is a useful tool in the “tri-personality” of a lecturer and a researcher. It provides a wealth of resources for optimum achievement of the ends targeted by the personality, within seconds; a student can retrieve information through the internet to update his/her knowledge, prepare his/her lecture, solve assignments given by lecturer or teacher and also work with colleagues and specialist(Scholastic, 2003).

RELATED PAPERS

Mohamed Gar-elnabi

Journal of the Neurological Sciences

Herman Adèr

International Journal of Intelligent Engineering and Systems

neelu khare

Deborah Letourneau

Revista De Educacao Puc Campinas

Jussara Reis

Revista Internacional de Pesquisa em Educação Matemática

Wagner Palanch

Revista Fe y Libertad

Mateo Echeverría Román

Revista General de Información y Documentación

Microbiology Research

paramageetham Chinthala

Cuadernos Geográficos

Tarik Bahaj

Luciana M Giovanni

Anales de la Facultad de Medicina

Rafael Reyes

International Journal of Surgery Case Reports

Maria Sofia Maria Sofia

Medical research archives

Ahmad Syafiq

International Archives of Medicine

Lais Vasconcelos

shubham jadhav

LORENA Abadía-Patiño

Military medicine

Katie Heinrich

ChemMedChem

ALFONSO MENDOZA LOSANA

Vlad Prokopenko

Biochemical and Biophysical Research Communications

Angela Messina

Environmental Science and Pollution Research

Aly Derbalah

heffield毕业证书 谢菲尔德大学毕业证

Journal of microbiology, biotechnology and food sciences

Arifa Tahir

Dr Nisreen Hoosain

See More Documents Like This

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

Read our research on: Gun Policy | International Conflict | Election 2024

Regions & Countries

2. online activities in emerging and developing nations.

Internet Use a Daily Activity for Most

Half or more of internet users in 27 of 32 emerging and developing nations say they use the internet daily. The most avid internet consumers are found in Chile and Lebanon, where 83% of internet users say they use it once a day or more. And three-quarters or more of users in Poland, Jordan, Tunisia, Argentina and Brazil use the internet in their daily lives.

Meanwhile, only about a third of internet users in Nicaragua (32%) and Uganda (32%) access the internet every day. And 37% of Filipino and 38% of Senegalese internet users say the same.

As with other aspects of internet use, younger users are much more likely to say they access the internet daily, compared with older people. For example, 95% of internet users in Lebanon under 35 years of age say they access the internet daily, while only 67% of those over 34 years say the same. In all, there is a significant age gap on daily internet use in 19 of the countries surveyed.

Internet Activities: Socializing Most Popular

When asked about various online activities, internet users in emerging and developing nations are clear that one activity in particular, staying in touch with friends and family, is the most popular. Overall, a median of 86% across the nations surveyed say they have contacted close relations via the internet in the past year. In fact, across the eight activities tested, staying in touch with friends and family is the predominant choice in every country surveyed (excluding Pakistan, which had insufficient sample size for internet activity analysis). Of those online, 100% of Senegalese, 93% of Ukrainians and 92% of Chileans socialize with family and friends online. Indian internet users are the least likely to say they do this, though still 63% of the online population use the internet to socialize.

Internet Activities: Getting Information Is Common

Getting various types of information, such as political news, health information and government services, is the next tier of internet use. A median of 54% among internet users in emerging and developing nations say they get political news online. Fully eight-in-ten Ukrainian and Vietnamese internet users say they get information about politics online. And 72% of internet users in Tunisia, 70% in Lebanon and 68% in Egypt say they get political information from the Web. Six-in-ten or more of online people in Russia (68%), Poland (66%), Kenya (62%) and China (62%) get political information online.

A median of about four-in-ten internet users (42%) in emerging and developing nations use the internet to get information about government or public services. This includes half or more of internet users in Tunisia, Russia, Tanzania, Senegal, Nigeria and Ukraine.

Internet Activities: Online Career and Commerce Less Common

Participation in commerce and career advancement is in the bottom tier of internet activities within emerging and developing nations. In this category, looking or applying for a job is the most common activity, representing a median of 35% among internet users across the countries surveyed. More than half of internet users in Bangladesh (62%), India (55%) and Kenya (53%) say they have looked or applied for a job online in the past 12 months, but only 18% in Lebanon say the same.

A median of only 22% conduct financial transactions online, but there is great variation on this activity. For instance, two-thirds of internet users in Poland make or receive payments online. And in one of the largest global financial markets, China, 44% of internet users say they use online banking in some form. Online payments are also more common among adult internet users in Tanzania, Chile, Russia and Kenya (where many make or receive payments with their cell phones ).

As is the case with online banking, few internet users in emerging and developing nations (a median of 16%) say they have bought a product online in the last year. However, the activity is much more common in China, one of the largest online global shopping markets . About half (52%) of online Chinese say they have a bought a product in the last 12 months. This is the highest percentage in this category among the countries surveyed besides Poland (58%).

The least common activity online among the eight tested is taking an online class or course that leads to a certificate. A median of only 13% among internet users in emerging and developing nations say they have taken a class in the past year.

Men Use Internet for Politics and Young Prefer Online Job Hunting, Socializing

Similar numbers of online adults ages 18 to 34 and 35 and older, with few exceptions, use the internet to get health information, news about politics, or information on public services; buy products; do online banking; or take online classes.

However, there are larger differences when it comes to staying in touch with friends and family and looking or applying for jobs. In 19 countries, internet users ages 18 to 34 use the internet to stay in touch with close friends or relatives more frequently than those 35 and older. And adults under the age of 35 use the internet to look for job opportunities more frequently than their older counterparts in 20 emerging and developing countries.

Social Networking Very Popular Among Internet Users

Socializing among internet users also applies to accessing social networks, and this is a very popular activity. Among internet users in the emerging and developing countries surveyed, a median of 82% use their internet connections to access social networking sites, such as Facebook, Twitter and other country-specific platforms.

The only countries surveyed where less than two-thirds of online adults use social networking sites are India, where 65% of internet users say they use social networks, Poland (62%) and China (58%).

As with overall internet access, social networking is more popular among young people than among those ages 35 and older. In the most extreme example, 85% of Poles ages 18-34 who have internet access say they use social networks. Only 44% of older Poles say the same, an age gap of 41 percentage points. Large and significant age gaps on social media usage appear in 22 of the countries surveyed.

Sharing Views about Music, Movies and Sports Popular on Social Networks

Among social networkers in emerging and developing nations, the most common online activity, besides staying in touch with friends and family , is sharing views about music and movies. But majorities also use social networks to talk about sports. Less discussed topics include the products people use, politics and religion.

Overall, a median of 72% of social networkers in emerging and developing nations say they use these platforms to share views about music and movies. In fact, among the items tested, this is the top use of social media in 26 of the countries surveyed. Talking about music and movies is especially popular among social networkers in Vietnam (88%), Thailand (86%), China (83%) and Mexico (83%). But half or more of social networkers in every country surveyed say they participate in sharing views about music and movies.

Sharing views about sports is also popular. A median of 56% of social networkers say they have talked about sports on social media sites. Talking about sports is popular in Africa and Asia, including 73% of social networking users in Kenya and 71% in Ghana. And 72% of Indian social networking users are keen on sharing views about this topic.

When it comes to politics, Middle Easterners share information with their friends and family with greater frequency. In Lebanon, three-quarters of social networking users say they share information about politics on these platforms. And 66% of Egyptians and 63% of Jordanian social networkers agree.

Religion is the least shared topic. But 64% of social networking users in Jordan and 58% each in Nigeria and Egypt say they share their views about religion online.

Male social networkers are much more likely to say they use the sites to share views about sports compared with their female counterparts. For example, in Tunisia, 82% of male social networkers say they talk about sports but only 31% of females do. And overall, men who use social networks share views about sports more often than women users in 25 countries.

And while there are age differences in some of these countries among the social networking activities tested, they are particularly pronounced in sharing views about music and movies. In 16 countries with sufficient sample sizes to analyze, 18- to 34-year-olds are more likely to say they use social networks to share views about music and movies than those 35 and older.

Add Pew Research Center to your Alexa

Say “Alexa, enable the Pew Research Center flash briefing”

Report Materials

Table of contents, 8 charts on technology use around the world, comparing views of the u.s. and china in 24 countries, how the political typology groups compare, bhutanese in the u.s. fact sheet, in changing u.s. electorate, race and education remain stark dividing lines, most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

This paper is in the following e-collection/theme issue:

Published on 1.4.2024 in Vol 26 (2024)

Response of Unvaccinated US Adults to Official Information About the Pause in Use of the Johnson & Johnson–Janssen COVID-19 Vaccine: Cross-Sectional Survey Study

Response of unvaccinated us adults to official information about the pause in use of the johnson & johnson–janssen covid-19 vaccine: cross-sectional survey study.

Authors of this article:

Research Letter

• Vishala Mishra 1 * , MBBS, MMCi   ; 
• Joseph P Dexter 2, 3, 4 * , PhD  

1 Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, United States

2 Data Science Initiative, Harvard University, Allston, MA, United States

3 Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, United States

4 Institute of Collaborative Innovation, University of Macau, Taipa, Macao

*all authors contributed equally

Corresponding Author:

Joseph P Dexter, PhD

Data Science Initiative

Harvard University

Science and Engineering Complex 1.312-10

150 Western Avenue

Allston, MA, 02134

United States

Phone: 1 8023381330

Email: [email protected]

Using a rapid response web-based survey, we identified gaps in public understanding of the Centers for Disease Control and Prevention’s messaging about the pause in use of the Johnson & Johnson–Janssen COVID-19 vaccine and estimated changes in vaccine hesitancy using counterfactual questions.

Introduction

On April 13, 2021, the Centers for Disease Control and Prevention (CDC) and Food and Drug Administration recommended a pause in use of the Johnson & Johnson (J&J)–Janssen COVID-19 vaccine due to 6 reports of cerebral venous sinus thrombosis in recently vaccinated individuals [ 1 ]. The announcement of the pause required development of a coordinated communication strategy under extreme time pressure and careful messaging by stakeholders to mitigate reduced public confidence in COVID-19 vaccines [ 2 ]. Moreover, official communication efforts had to consider the potential influence of already widespread misinformation about the vaccines on attitudes toward the pause [ 3 , 4 ]. In this survey study, we evaluated understanding and impressions of the CDC’s public web-based information about the J&J-Janssen pause among unvaccinated US adults.

Web-Based Survey About J&J-Janssen Pause

We administered the web-based survey to two cohorts of US adults recruited through Prolific between April 19-21, 2021 (cohort A), and April 21-23, 2021 (cohort B). Both cohorts were assembled using convenience sampling of unvaccinated adults. To obtain information about a population that especially needed targeted vaccine communication, the first cohort was restricted to individuals expressing neutral or negative sentiments about COVID-19 vaccines. The survey design and recruitment strategy are described in Multimedia Appendix 1 ; the survey questions are provided in Multimedia Appendices 2 and 3 .

Ethical Considerations

The study was approved by Harvard University’s Committee on the Use of Human Subjects (IRB20-2089), and participants agreed to a consent statement on the first page of the survey. Participants were paid US $2 for taking the survey. All study data were collected anonymously.

A total of 271 and 286 participants were included in cohorts A and B, respectively (demographic characteristics listed in Table 1 ). Across participants, the median number of correct responses to the comprehension questions was 6 in both cohort A (IQR 1.5; range 0-7) and cohort B (IQR 1.0; range 1-7). The total number of correct responses was negatively associated with intention not to seek vaccination in both cohort A (odds ratio 0.61, 95% CI 0.45-0.82; P =.001) and cohort B (odds ratio 0.48, 95% CI 0.31-0.74; P =.001; Multimedia Appendix 4 ). Although a majority of participants rated the passages as “clear and easy to read” (cohort A: n=229, 84.5%; cohort B: n=243, 85%), fewer indicated that they would be likely to share them on social media (cohort A: n=53, 19.6%; cohort B: n=75, 26.3%).

The web page mentioned “a small number of reports” of cerebral venous sinus thrombosis in individuals who received the J&J-Janssen vaccine. When asked to guess a specific number, 188 (69.4%) and 133 (46.5%) respondents in cohorts A and B, respectively, estimated 100 or more cases, at least an order of magnitude higher than the actual value; 176 (64.9%) and 128 (44.8%) respondents in cohorts A and B, respectively, estimated 10 or more deaths after vaccination ( Figure 1 ).

Responding to a counterfactual question, 127 (46.9%) and 139 (48.6%) participants in cohorts A and B, respectively, indicated that the pause reduced their confidence in the J&J-Janssen vaccine’s safety ( Figure 1 ). Most participants reported no change in their confidence in COVID-19 vaccines’ safety in general (cohort A: n=182, 67.2%; cohort B: n=194, 67.8%) or intention to receive the Pfizer-BioNTech or Moderna vaccine (cohort A: n=206, 76%; cohort B: n=211, 73.8%).

a Participants could select more than one option.

b Includes participants who selected “American Indian or Alaska Native,” “Native Hawaiian or Other Pacific Islander,” or “Another option not listed here.”

c Number of participants who gave the correct answer to each question.

d Number of participants who gave the indicated number of correct answers across all questions.

e Number of participants who answered “Strongly agree” or “Agree” about each description on a 6-point Likert scale.

In our web-based survey about the CDC’s messaging around the J&J-Janssen vaccine pause, many respondents overestimated the number of case reports that prompted the pause, often by several orders of magnitude. Since verbal descriptors are elastic concepts that can be misinterpreted, grounding them with numbers can reduce variability in risk perception and promote informed decision-making [ 5 ].

Respondents also expressed reduced confidence in the safety of the J&J-Janssen vaccine, highlighting the potential danger of conveying piecemeal information about risk during a pandemic response [ 3 ]. Encouragingly, the reduced confidence did not extend to mRNA COVID-19 vaccines, consistent with previous findings that overall vaccine hesitancy remained stable following the pause [ 6 ]. These results were obtained using the counterfactual format, which is less susceptible to overestimating shifts in beliefs than the change format ( Multimedia Appendix 1 ). The negative association between understanding of the passage and self-reported vaccine hesitancy suggests that more targeted messaging may have been useful to promote vaccine confidence [ 7 , 8 ].

Consistent with uncertainty management theory [ 9 ], individuals likely viewed the pause in different ways, leading to a spectrum of emotional responses and changes in behavior. Despite being a safety precaution, the pause introduced new uncertainties requiring effective management through clear and consistent messaging, highlighting the balance that must be maintained between fostering trust and preventing unnecessary alarm [ 10 ]. Given the limitations of the deficit model of scientific communication [ 11 ], just providing technically correct information is insufficient for effective communication during public health crises. Instead, attention should be given to the accessibility of information across diverse socioeconomic groups, in line with the knowledge gap hypothesis [ 12 ], and to countering misinformation by providing easy-to-use official guidance [ 6 , 7 ].

The study is limited by the convenience sampling strategy; the participants recruited were not representative of the US population as a whole, and the findings should not be generalized to other contexts. Since the study was conducted on the web, individuals with lower internet and health literacy may have been excluded.

Acknowledgments

We thank Vasudha Mishra, MBBS, for assistance with graphic design. This work was supported by a CoronaVirusFacts Alliance Grant from the Poynter Institute, a Harvard Data Science Fellowship, and the Institute of Collaborative Innovation at the University of Macau.

Data Availability

The data sets generated during this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

None declared.

Additional information about survey methodology.

Survey administered to cohort A.

Survey administered to cohort B.

Supplemental tables about survey questions and ordinal logistic regression analysis.

• Karron RA, Key NS, Sharfstein JM. Assessing a rare and serious adverse event following administration of the Ad26.COV2.S vaccine. JAMA. Jul 22, 2021;325(24):2445-2447. [ CrossRef ] [ Medline ]
• Wood S, Schulman K. Beyond politics—promoting Covid-19 vaccination in the United States. N Engl J Med. Mar 18, 2021;384(7):e23. [ CrossRef ] [ Medline ]
• Lee JJ, Kang K, Wang MP, Zhao SZ, Wong JYH, O'Connor S, et al. Associations between COVID-19 misinformation exposure and belief with COVID-19 knowledge and preventive behaviors: cross-sectional online study. J Med Internet Res. Dec 13, 2020;22(11):e22205. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Ginossar T, Cruickshank IJ, Zheleva E, Sulskis J, Berger-Wolf T. Cross-platform spread: vaccine-related content, sources, and conspiracy theories in YouTube videos shared in early Twitter COVID-19 conversations. Hum Vaccin Immunother. Dec 31, 2022;18(1):1-13. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Edwards A, Elwyn G, Mulley A. Explaining risks: turning numerical data into meaningful pictures. BMJ. May 06, 2002;324(7341):827-830. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hsieh YL, Rak S, SteelFisher GK, Bauhoff S. Effect of the suspension of the J&J COVID-19 vaccine on vaccine hesitancy in the United States. Vaccine. Jan 24, 2022;40(3):424-427. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Miller M, Castrucci BC. Changing the COVID-19 conversation: it's about language. JAMA Health Forum. Mar 01, 2021;2(2):e210020. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Mishra V, Dexter JP. Comparison of readability of official public health information about COVID-19 on websites of international agencies and the governments of 15 countries. JAMA Netw Open. Aug 03, 2020;3(8):e2018033. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Brashers D. Communication and uncertainty management. J Commun. 2001;51(3):477-497. [ CrossRef ]
• Petersen MB, Bor A, Jørgensen F, Lindholt MF. Transparent communication about negative features of COVID-19 vaccines decreases acceptance but increases trust. Proc Natl Acad Sci U S A. Jul 20, 2021;118(29):e2024597118. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Reincke CM, Bredenoord AL, van Mil MH. From deficit to dialogue in science communication: the dialogue communication model requires additional roles from scientists. EMBO Rep. Oct 03, 2020;21(9):e51278. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lind F, Boomgaarden HG. What we do and don’t know: a meta-analysis of the knowledge gap hypothesis. Ann Int Commun Assoc. May 09, 2019;43(3):210-224. [ CrossRef ]

Abbreviations

Edited by A Mavragani; submitted 25.08.22; peer-reviewed by M Graham, T Ginossar, A Scherer; comments to author 25.01.23; revised version received 26.05.23; accepted 29.12.23; published 01.04.24.

©Vishala Mishra, Joseph P Dexter. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 01.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

University of South Florida

Main Navigation

USF research reveals language barriers limit effectiveness of cybersecurity resources

• April 1, 2024

Research and Innovation

By: John Dudley , University Communications & Marketing

The idea for Fawn Ngo’s latest research came from a television interview.

Ngo, a University of South Florida criminologist, had spoken with a Vietnamese language network in California about her interest in better understanding how people become victims of cybercrime.

Afterward, she began receiving phone calls from viewers recounting their own experiences of victimization.

Fawn Ngo, associate professor in the USF College of Behavioral and Community Sciences

“Some of the stories were unfortunate and heartbreaking,” said Ngo, an associate professor in the USF College of Behavioral and Community Sciences. “They made me wonder about the availability and accessibility of cybersecurity information and resources for non-English speakers. Upon investigating further, I discovered that such information and resources were either limited or nonexistent.”

The result is what’s believed to be the first study to explore the links among demographic characteristics, cyber hygiene practices and cyber victimization using a sample of limited English proficiency internet users.

Ngo is the lead author of an article, “Cyber Hygiene and Cyber Victimization Among Limited English Proficiency (LEP) Internet Users: A Mixed-Method Study,” which just published in the journal Victims & Offenders. The article’s co-authors are Katherine Holman, a USF graduate student and former Georgia state prosecutor, and Anurag Agarwal, professor of information systems, analytics and supply chain at Florida Gulf Coast University. 

Their research, which focused on Spanish and Vietnamese speakers, led to two closely connected main takeaways:

• LEP internet users share the same concern about cyber threats and the same desire for online safety as any other individual. However, they are constrained by a lack of culturally and linguistically appropriate resources, which also hampers accurate collection of cyber victimization data among vulnerable populations.
• Online guidance that provides the most effective educational tools and reporting forms is only available in English. The most notable example is the website for the Internet Crime Complaint Center, which serves as the FBI’s primary apparatus for combatting cybercrime.

As a result, the study showed that many well-intentioned LEP users still engage in such risky online behaviors as using unsecured networks and sharing passwords. For example, only 29 percent of the study’s focus group participants avoided using public Wi-Fi over the previous 12 months, and only 17 percent said they had antivirus software installed on their digital devices.

Previous research cited in Ngo’s paper has shown that underserved populations exhibit poorer cybersecurity knowledge and outcomes, most commonly in the form of computer viruses and hacked accounts, including social media accounts. Often, it’s because they lack awareness and understanding and isn’t a result of disinterest, Ngo said.

“According to cybersecurity experts, humans are the weakest link in the chain of cybersecurity,” Ngo said. “If we want to secure our digital borders, we must ensure that every member in society, regardless of their language skills, is well-informed about the risks inherent in the cyber world.”

The study’s findings point to a need for providing cyber hygiene information and resources in multiple formats, including visual aids and audio guides, to accommodate diverse literacy levels within LEP communities, Ngo said. She added that further research is needed to address the current security gap and ensure equitable access to cybersecurity resources for all internet users.

In the meantime, Ngo is working to create a website that will include cybersecurity information and resources in different languages and a link to report victimization.

“It’s my hope that cybersecurity information and resources will become as readily accessible in other languages as other vital information, such as information related to health and safety,” Ngo said. “I also want LEP victims to be included in national data and statistics on cybercrime and their experiences accurately represented and addressed in cybersecurity initiatives.” 

Return to article listing

Cybersecurity , John Dudley , USF Sarasota-Manatee

News Archive

Learn more about USF's journey to Preeminence by viewing Newsroom articles from past years.

USF in the News

Npr: a professor worried no one would read an algae study. so she had it put to music.

April 4, 2024

Tampa Bay Times: Tampa doctors fit device that restores arm movement to stroke victims

March 27, 2024

Associated Press: Debate emerges over whether modern protections could have saved Baltimore bridge

Inside higher ed: university of south florida launching ai college.

March 22, 2024

More USF in the News

Secured IIoT against trust deficit - A flexi cryptic approach

• Published: 09 April 2024

Cite this article

• V. M. Padmapriya 1 ,
• K. Thenmozhi 2 ,
• M. Hemalatha 3 ,
• V. Thanikaiselvan 4 ,
• C. Lakshmi 2 ,
• Nithya Chidambaram 2 &
• Amirtharajan Rengarajan   ORCID: orcid.org/0000-0003-1574-3045 2  

This research allows the secure surveillance approach for the Internet of Things (IoT) methodology to be developed by integrating wireless signalling and image encryption strategy. Since the Cloud Service Telco (CST) is a semi-trusted body in cloud services, user data is encrypted before uploading to a cloud server for data protection from disclosure. The flexibility of encrypted data sharing is essential for cloud storage users. This study investigates the Discrete Wavelet Transform (DWT) technique with modified Huffman compression and Elliptic Curve Cryptography (ECC). It encrypts and decrypts the data and enhances industrial security surveillance in transmission. It uses the wireless network’s next generation (5G or 6G) as uplink Single Carrier Frequency Division Multiple Access (SC-FDMA) strategies via the IoT. This study presented a novel approach to proposing hardware architecture for a secure web camera integrated with the Atmel in the mega AVR family (ATMEGA) microcontroller, suitable for IoT applications. The experimental results confirm the proposed model’s efficacy compared with existing robustness and security analysis algorithms. These systems are also used by implementing industry-standard protocols using IoTs to monitor industrial applications. The proposed framework can also minimise bandwidth, transmission cost, storage space, tracking data, and decisions about abnormal events such as potential fraud and extinguisher detection in surveilling applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request.

Yavari A, Jayaraman PP, Georgakopoulos D (2016) Contextualised service delivery in the internet of things: parking recommender for smart cities. In: 2016 IEEE 3Rd world forum on internet of things (WF-iot). IEEE, pp 454–459. https://doi.org/10.1109/WF-IoT.2016.7845479

Tan Ya, Zhang X, Sharif K, Liang C, Zhang Q, Li Y (2018) Covert timing channels for iot over mobile networks. IEEE Wirel Commun 25(6):38–44

Article   Google Scholar  

Guan Z, Li J, Wu L, Zhang Y, Wu J, Du X (2017) Achieving efficient and secure data acquisition for cloud-supported internet of things in smart grid. IEEE Internet Things J 4(6):1934–1944

Xu LD, He W, Li S (2014) Internet of things in industries: a survey. IEEE Trans Industr Inf 10(4):2233–2243

Liu H, Liu J, Ma C (2023) Constructing dynamic strong S-Box using 3D chaotic map and application to image encryption. Multimed Tools Appl 82:23899–23914. https://doi.org/10.1007/s11042-022-12069-x

Liu H, Kadir A, Xu C (2020) Cryptanalysis and constructing S-box based on chaotic map and backtracking. Appl Math Comput 376:125153

MathSciNet   Google Scholar  

Lloret J, Bosch I, Sendra S, Serrano A (2011) A wireless sensor network for vineyard monitoring that uses image processing. Sensors 11(6):6165–6196

Li G, Wu J, Li J, Wang K, Ye T (2018) Service popularity-based smart resources partitioning for fog computing-enabled industrial internet of things. IEEE Trans Industr Inf 14(10):4702–4711

Severino R, Rodrigues J, Alves J, Ferreira LL (2023) Performance assessment and mitigation of timing covert channels over the IEEE 802.15.4. J Sens Actuator Netw 12(4):60. https://doi.org/10.3390/jsan12040060

Alnajim AM, Habib S, Islam M, Thwin SM, Alotaibi F (2023) A comprehensive survey of cybersecurity threats, attacks, and effective countermeasures in Industrial Internet of things. Technologies 11(6):161. https://doi.org/10.3390/technologies11060161

Zanella A, Bui N, Castellani A, Vangelista L, Zorzi M (2014) Internet of things for smart cities. IEEE Internet Things J 1(1):22–32

Habib K, Torjusen A, Leister W (2014) A novel authentication framework based on biometric and radio fingerprinting for the iot in ehealth. In: SMART 2014: The Third International Conference on Smart Systems, Devices, and Technologies, pp 32–37

Mehmood I, Sajjad M, Ejaz W, Baik SW (2015) Saliency-directed prioritisation of visual data in wireless surveillance networks. Inform Fusion 24:16–30

Muhammad K, Hamza R, Ahmad J, Lloret J, Wang H, Baik SW (2018) Secure surveillance framework for iot systems using probabilistic image encryption. IEEE Trans Industr Inf 14(8):3679–3689

Innovative method leads to smaller, cheaper IoT. https://phys.org/news/2018-11-method-smaller-cheaper-iot-sensors.html

Bobbio A, Campanile L, Gribaudo M, Iacono M, Marulli F, Mastroianni M (2023) A cyber warfare perspective on risks related to health iot devices and contact tracing. Neural Comput & Applic 35:13823–13837. https://doi.org/10.1007/s00521-021-06720-1

Johnson M, Ishwar P, Prabhakaran V, Schonberg D, Ramchandran K (2004) On compressing encrypted data. IEEE Trans Signal Process 52(10):2992–3006

Article   MathSciNet   Google Scholar  

Castiglione A, Choo KKR, Nappi M, Ricciardi S (2017) Context aware ubiquitous biometrics in edge of military things. IEEE Cloud Comput 4(6):16–20

Azmoodeh A, Dehghantanha A, Choo KKR (2018) Robust malware detection for internet of (battlefield) things devices using deep eigenspace learning. IEEE Trans Sustain Comput 4(1):88–95

Sadeghi AR, Wachsmann C, Waidner M (2015) Security and privacy challenges in industrial internet of things. In: 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC), IEEE, pp 1–6

Esposito C, Castiglione A, Martini B, Choo KKR (2016) Cloud manufacturing: security, privacy, and forensic concerns. IEEE Cloud Comput 3(4):16–22

Choo KKR, Gritzalis S, Park JH (2018) Cryptographic solutions for industrial internet-of-things: research challenges and opportunities. IEEE Trans Industr Inf 14(8):3567–3569

Li S, Da Xu L, Zhao S (2018) 5 g internet of things: a survey. J Industrial Inform Integr 10:1–9

Google Scholar  

Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54(15):2787–2805

Evangelos AK, Nikolaos DT, Anthony CB (2011) Integrating rfids and smart objects into a unified internet of things architecture. Adv Internet Things 1:5–12. https://doi.org/10.4236/ait.2011.11002

Roy C, Misra S, Pal S (2020) Blockchain-enabled safety-as-a-service for industrial iot applications. IEEE Internet Things Magazine 3(2):19–23

Gavrilă C, Popescu V, Alexandru M, Murroni M, Sacchi C (2020) An sdr- based satellite gateway for internet of remote things (iort) applications. IEEE Access 8:115423–115436

Awaisi KS, Hussain S, Ahmed M, Khan AA, Ahmed G (2020) Leveraging iot and fog computing in healthcare systems. IEEE Internet Things Magazine 3(2):52–56

Elhoseny M, Ramírez-González G, Abu-Elnasr OM, Shawkat SA, Arunkumar N, Farouk A (2018) Secure medical data transmission model for iot-based healthcare systems. IEEE Access 6:20596–20608

Singh A, Chawla N, Ko JH, Kar M, Mukhopadhyay S (2018) Energy efficient and side-channel secure cryptographic hardware for iot-edge nodes. IEEE Internet Things J 6(1):421–434

Lu Y (2017) Industry 4.0: a survey on technologies, applications and open re- search issues. J Ind Info Integr 6:1–10

Cheng J, Chen W, Tao F, Lin CL (2018) Industrial Iot in 5 g environment towards smart manufacturing. J Ind Inf Integr 10:10–19. https://doi.org/10.1016/j.jii.2018.04.001

Yuehong Y, Zeng Y, Chen X, Fan Y (2016) The internet of things in healthcare: an overview. J Ind Inf Integr 1:3–13

Padmapriya V, Thenmozhi K, Praveenkumar P, Amirtharajan R (2020) Ecc joins first time with SC_FDMA for mission security. Multimed Tools Appl 79(25):17945–17967

Li J, Chen L, Cai W, Xiao J, Zhu J, Hu Y, Wen K (2022) Holographic encryption algorithm based on bit-plane decomposition and hyperchaotic lorenz system. Opt Laser Technol 152:108127. https://doi.org/10.1016/j.optlastec.2022.108127

Padmapriya V, Thenmozhi K, Praveenkumar P, Amirtharajan R (2022) Misconstrued voice on SC-FDMA for secured comprehension a cooperative influence of DWT and ECC. Multimed Tools Appl 81:7201–7217. https://doi.org/10.1007/s11042-022-11996-z

Sujihelen L, Jayakumar C (2018) Inclusive elliptical curve cryptography (iecc) for wireless sensor network efficient operations. Wireless Pers Commun 99(2):893–914

Britto J, Roja MM (2017) Gaussian noise analysis in elliptic curve encrypted images. In: 2017 International Conference on Intelligent Sustainable Systems (ICISS), IEEE, pp 791–794

El-Latif AAA, Li L, Niu X (2014) A new image encryption scheme based on cyclic elliptic curve and chaotic system. Multimed Tools Appl 70(3):1559–1584

Khanzadi H, Eshghi M, Borujeni SE (2014) Image encryption using random bit sequence based on chaotic maps. Arab J Sci Eng 39(2):1039–1047

Li L, Abd El-Latif AA, Niu X (2012) Elliptic curve elgamal based homomorphic image encryption scheme for sharing secret images. Sig Process 92(4):1069–1078

Dosselmann R, Yang XD (2011) A comprehensive assessment of the structural similarity index. Signal Image Video Process 5(1):81–91

Rahadjo B et al (2017) Design and implementation stegocrypto based on elgamal elliptic curve. In: 2017 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), IEEE, pp 95–99

Lohachab A et al (2019) Ecc based inter-device authentication and authorisation scheme using MQTT for iot networks. J Inform Secur Appl 46:1–12

Tawalbeh L, Mowafi M, Aljoby W (2013) Use of elliptic curve cryptography for multimedia encryption. IET Inf Secur 7(2):67–74

Norouzi B, Seyedzadeh SM, Mirzakuchaki S, Mosavi MR (2015) A novel image encryption based on row-column, masking and main diffusion processes with hyper chaos. Multimedia Tools Appl 74(3):781–811

Sokouti M, Sokouti B (2018) A prisma-compliant systematic review and analysis on color image encryption using dna properties. Comput Sci Rev 29:14–20

Wang S, Peng Q, Du B (2022) Chaotic color image encryption based on 4d chaotic maps and dna sequence. Opt Laser Technol 148:107753

Alarood AA, Alsolami E, Al-Khasawneh MA, Ababneh N, Elmedany W (2022) Ies: Hyper-chaotic plain image encryption scheme using improved shuffled confusion-diffusion. Ain Shams Eng J 13(3):101583

Duan CF, Zhou J, Gong LH, Wu JY, Zhou NR (2022) New color image encryption scheme based on multi-parameter fractional discretechebyshev moments and nonlinear fractal permutation method. Opt Lasers Eng 150:106881

Zhou W, Wang X, Wang M, Li D (2022) A new combination chaotic system and its application in a new bit-level image encryption scheme. Opt Lasers Eng 149:106782

Mittal A, Moorthy AK, Bovik AC (2012) No-reference image quality assessment in the spatial domain. IEEE Trans Image Process 21(12):4695–4708

Mittal A, Moorthy AK, Bovik AC (2011) Blind/referenceless image spatial quality evaluator. In: 2011 Conference Record of the Forty Fifth Asilo- mar Conference on Signals, Systems and Computers (ASILOMAR), IEEE, pp 723–727

Mittal A, Soundararajan R, Bovik AC (2012) Making a completely blind image quality analyser. IEEE Signal Process Lett 20(3):209–212

Sridevi A, Sivaraman R, Balasubramaniam V, Sreenithi, Siva J, Thanikaiselvan V, Rengarajan A (2022) On chaos based duo confusion duo diffusion for colour images. Multimedia Tools Appl 81(12):16987–17014. https://doi.org/10.1007/s11042-022-12471-5

Zhang F, Zhang X, Cao M, Ma F, Li Z (2021) Characteristic analysis of 2D lag-Complex Logistic Map and its application in image encryption. IEEE Multimedia 28(4):96–106. https://doi.org/10.1109/MMUL.2021.3080579

Lone MA, Qureshi S (2022) RGB image encryption based on symmetric keys using arnold transform, 3D chaotic map and affine hill cipher. Optik 260:168880. https://doi.org/10.1016/j.ijleo.2022.168880

Teng L, Wang X, Xian Y (2022) Image encryption algorithm based on a 2D-CLSS hyperchaotic map using simultaneous permutation and diffusion. Inf Sci 605:71–85. https://doi.org/10.1016/j.ins.2022.05.032

Ye G, Wu H, Liu M, Shi Y (2022) Image encryption scheme based on blind signature and an improved lorenz system. Expert Syst Appl 205:117709. https://doi.org/10.1016/j.eswa.2022.117709

Aparna H, Bhumijaa B, Santhiyadevi R, Vaishanavi K, Sathanarayanan M, Rengarajan A, Abd El-Latif AA (2021) Double layered fridrich structure to conserve medical data privacy using quantum cryptosystem. J Inform Secur Appl 63:102972. https://doi.org/10.1016/j.jisa.2021.102972

Padmapriya VM, Thenmozhi K, Avila J, Amirtharajan R, Praveenkumar P (2020) Real time authenticated spectrum access and encrypted image transmission via cloud enabled fusion centre. Wirel Personal Commun 115(3):2127–2148

Manikandan V, Amirtharajan R (2022) A simple embed over encryption scheme for DICOM images using Bülban Map. Med Biol Eng Comput 60(3):701–717

Zhang Y, Xie H, Sun J, Zhang H (2022) An efficient multi-level encryption scheme for stereoscopic medical images based on coupled chaotic system and otsu threshold segmentation. Comput Biol Med 146:105542. https://doi.org/10.1016/j.compbiomed.2022.105542

https://www.youtube.com/watch?v=6bfY9JXOppI . Accessed on 06/01/2023

https://projecthub.arduino.cc/ . Accessed on 06/01/2023

Mahalingam H, Veeramalai T, Menon AR, Amirtharajan SS (2023) Dual-domain image encryption in Unsecure Medium—A Secure Communication Perspective. Mathematics 11:457. https://doi.org/10.3390/math11020457

Zhang X, Wang X (2019) Multiple-image encryption algorithm based on DNA encoding and chaotic system. Multimed Tools Appl 78:7841–7869. https://doi.org/10.1007/s11042-018-6496-1

Zhu S, Deng X, Zhang W, Zhu C (2023) Image encryption Scheme based on newly designed chaotic map and parallel DNA coding. Mathematics 11:231. https://doi.org/10.3390/math11010231

Song X, Chen G, Abd El-Latif AA (2022) Quantum Color image encryption Scheme Based on Geometric Transformation and Intensity Channel Diffusion. Mathematics 10:3038. https://doi.org/10.3390/math10173038

Zhong H, Li G, Xu X, Song X (2022) Image encryption Algorithm based on a novel wide-range Discrete Hyperchaotic Map. Mathematics 10:2583. https://doi.org/10.3390/math10152583

Lai Q, Liu Y, Yang L (2023) Image encryption using memristive hyperchaos. Appl Intell. https://doi.org/10.1007/s10489-023-04727-w

Download references

Acknowledgements

The authors gratefully acknowledge the technical and financial support provided by the Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia.

Institutional Fund Projects under grant no. IFPIP: 974-144-1443. Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia.

Author information

Authors and affiliations.

Department of Computer Sciences, Marquette University, Milwaukee, WI, 53233, USA

V. M. Padmapriya

School of Electrical & Electronics Engineering, SASTRA Deemed University, Thanjavur, 613401, India

K. Thenmozhi, C. Lakshmi, Nithya Chidambaram & Amirtharajan Rengarajan

Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 22254, Saudi Arabia

M. Hemalatha

School of Electronics Engineering, Vellore Institute of Technology, Vellore, 632014, India

V. Thanikaiselvan

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Amirtharajan Rengarajan .

Ethics declarations

Ethics approval.

This article does not contain any studies with human participants performed by authors.

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Padmapriya, V.M., Thenmozhi, K., Hemalatha, M. et al. Secured IIoT against trust deficit - A flexi cryptic approach. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18962-x

Download citation

Received : 11 July 2023

Revised : 01 January 2024

Accepted : 13 March 2024

Published : 09 April 2024

DOI : https://doi.org/10.1007/s11042-024-18962-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Discrete Wavelet Transform
• Elliptic Curve Cryptography
• Internet of Things
• Single Carrier Frequency Division Multiple access
• Find a journal
• Publish with us
• Track your research

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List

Internet Use and Its Impact on Engagement in Leisure Activities in China

Ronggang zhou.

1 School of Economics and Management, Beihang University, Beijing, P. R. China

Patrick S. W. Fong

2 Department of Building & “Real Estate”, Hong Kong Polytechnic University, Hong Kong, P. R. China

3 Research & Development, Greater China, Millward Brown, Beijing, P. R. China

Conceived and designed the experiments: RZ PT. Performed the experiments: RZ PT. Analyzed the data: RZ. Contributed reagents/materials/analysis tools: RZ. Wrote the paper: RZ. Contributed literature review and edited manuscript: PF.

Introduction

Internet use has become an increasingly common leisure time activity among Chinese citizens. The association between Internet use and engagement in leisure activities is especially unclear among China population. This study aims to investigate Internet usage and to determine whether active Internet use is a marker for low or high levels of leisure time activities.

Methods/Principal Findings

With the use of a face-to-face structured questionnaire interview, a total of 2,400 respondents who met all screening requirements were surveyed to answer the questions in eight major cities in China. 66.2% ( n  = 1,589) of all respondents were identified as Internet users. Of these Internet users, 30.0%, 24.1%, 26.4%, and 19.6% were clustered as “informative or instrumental users,” “entertainment users,” “communication users,” and “advanced users,” respectively. Regarding time spent on Internet use in leisure time, more than 96% reported going online in non-work situations, and 26.2% ( n  = 416) were classified as “heavy Internet users.” A logistic regression analysis revealed that there were significant differences in some leisure activities between non-Internet users and Internet users, with an observed one-unit increase in the leisure time dependence category increasing the probability of engaging in mental or social activities. In contrast, Internet users were less engaged in physical exercise-related activities. In addition, advanced Internet users were generally more active in leisure time activities than non-Internet users and other types of users.

Conclusion/Significance

Internet use is one of very common leisure activities in Chinese citizens, and age, gender, income, and education are the key factors affecting Internet access. According to different types of leisure activities, Internet usage has different impacts on leisure activity engagement. High Internet dependence has no significant negative influence on engagement in mental or social leisure activities, but this group respondent tended to be less engaged in physical activities.

Leisure activity can be defined as the voluntary use of free time for activities outside the daily routine, and it is one of the major components of a healthy lifestyle [1] . Engagement in leisure activities provides opportunities to meet life values and needs and contributes to subjective well-being [2] , [3] , [4] , [5] . Currently, understanding and making better use of the Internet to improve users’ quality of life is an important research focus [6] . The Internet continues to be used worldwide and has changed the pattern of life in recent decades.

According to the 31 th statistical report on Internet development, China’s Internet users, who aged at six or above and have used Internet within past six months, included 564 million users by the end of 2012. Of these users, the largest group is made up of urban residents (72.4%) [7] . Internet use has become an increasingly common leisure time activity in this population. Unfortunately, Internet use, especially with regard to sociability, has been thought to be inversely linked to quality of life [3] . One of potential reasons for this negative effect on quality of life is the imbalanced allocation of time between Internet use and other regular leisure activities. By changing Internet use patterns and spending suitable amounts of time on regular leisure activities, quality of life may be enhanced [3] , [8] , [9] . Although a significant body of research has focused on understanding this issue, the association between Internet use and leisure activity engagement is still a controversial issue. Therefore, it is important to understand Internet usage and then to determine whether active and high Internet use is a marker for low or high levels of regular leisure time activities.

To answer this question, we must first understand the digital divide that is characteristic of Internet use. The use of the Internet bridges previously reported digital divide gaps, which can be illustrated by investigating the systematic differences in terms of socioeconomic variables, demographic variables, or socio-environmental factors between Internet users and those who do not access the Internet frequently. Broad survey studies confirm that the previously cited gaps are quickly disappearing [10] , [11] . However, the digital divide, in terms of demographic, socioeconomic, and educational differences in the access to and usage of the Internet, is still important for understanding Internet usage, especially in developing countries.

A related area of Internet use that has attracted investigators’ attention is Internet use patterns and users’ typology with respect to their use pattern. Several studies have demonstrated that people with similar levels of access engage the Internet in fundamentally different ways [12] , [13] . The issue is related to the user’s Internet usage pattern involves two important variables–online activities and time spent online. The increasing number of Internet users who spend more time online and engage in increasingly diverse activities has captured the attention of policy makers and social researchers [6] . According to previous studies [7] , [14] , [15] , web motives or online activities vary in many ways, including informative activities, social or communicatory activities, transaction activities, and entertainment activities. By integrating the degree of Internet access and online activities, Internet user types can be identified. For example, those who access the Internet with a very varied and broad Internet behavior have been termed “advanced users”, those who have the highest mean scores in goal-oriented activities (e.g., searching for information about goods or services) have been labeled as “instrumental users”, those who have the highest access Internet with regards to enjoyment activities such as downloading games or music have been clustered as “entertainment users,” and those who have occasional or no Internet access have been categorized as “sporadic users” or “non- users” [16] . Internet user types can be a useful way to describe Internet use patterns.

Although little research has systematically focused on the relationship between Internet use and leisure activity engagement, there is evidence that suggests the impact of Internet use on user activities in their leisure time. Based on the psychological perspective, leisure behaviors have a positive impact on cognitive function and dementia based on their physical, mental, or social aspects. Therefore, we tend to accept the classifications of “physical activity,” “mental activity,” and “social activity” [1] . With respect to the impact of the Internet on users’ social lives, major contradictory findings have been reported. For example, some research shows that increased Internet usage has been associated with a decline in users’ interactions with family members within the household and a reduced social circle [8] , [17] . In contrast, other studies have suggested that the Internet may have less of an impact on many aspects of social life than is frequently supposed and can actually enhance the social lives of its users [14] , [18] , [19] , [20] . With regard to the association between Internet use and physical activity, leisure time physical activity levels were largely independent of Internet and computer use among a sample of adults with an average age of 45 years [21] .

Despite efforts to understand the relationship between Internet use and leisure time social activities and physical activities, the cumulative results from these studies suggest that the situation is complex and controversial. In summary, (a) The time spent between Internet use and leisure activities is a key factor for investigating the impact of Internet use on leisure activities, especially when considering the engagement or dependence on the Internet for leisure purposes. However, few studies have been systematically conducted to examine the association between leisure time Internet use and other leisure behavior. (b) The disagreements above may result from group or individual differences. Recently, Internet users’ typologies have attracted investigators’ attention [16] . The Internet “means different things to different people and is used in different ways for different purposes” [22] , and we believe that Internet use by different types of users with different purposes will have mixed impacts on other leisure activities.

To our knowledge, few studies have evaluated how leisure time Internet use and Internet user characteristics (e.g., user topology, gender, and age group) relate to other leisure time activities. The current study seeks for the first time to examine the associations between Internet use, specifically in leisure time, and leisure time activities in a large socially diverse sample of Chinese citizens. Thus, the aims of the present study were to 1) understand the Internet usage pattern in Chinese citizens, especially to address the factors that affect Internet access among this population; 2) investigate whether leisure time Internet use or dependence affects engagement in other leisure activities, and specifically in different gender and age groups; 3) compare the engagement in leisure activities among Internet user types including non-Internet users. In relation to healthy lifestyles and quality of life, this information will help provide direct evidence to clarify the controversial results in the field.

Research Methodology

Ethics statement.

This study was reviewed and approved by the committee for the protection of subjects at Millward Brown. Written consent was also obtained from each participant before administering the survey according to the established guidelines of the committee. The survey was entered in the records of the National Bureau of Statistics of China.

Participants

As shown in Table 1 , a total of 2,400 respondents completed full interviews and answered the corresponding questions. The respondents were from eight representative cities in China (Beijing, Shanghai, Guangzhou, Chengdu, Shenyang, Wuhan, Xian, and Fuzhou). According to our research design for sampling, the numbers of respondents were balanced well for gender (i.e., 1,200 males and females) and city (i.e., 800 participants who met the requirements were invited to answer the questions in each city). In addition, the participants were approximately balanced among age groups: 14–24 (27.3%), 25–34 (22.6%), 35–44 (22.6%), and 45–60 (27.5%). With respect to educational level, 21.7% of the respondents had a bachelor’s degree or above, 57.5% had a mid-level education (i.e., associate, secondary school, and high school), and 20.8% had less education (i.e., middle school or below). With regard to employee status and income, 16.8% of respondents were students, 16.4% reported no income, 7.2% earned less than 1,000 CNY monthly, 46.8% had a monthly income of between 1,000–2,999 CNY, and 29.6% had a monthly salary of 3,000 CNY or above. Each respondent was approached in a public place such as a supermarket by a trained interviewer. The respondents were informed about the study by the interviewer’s reading of a written introduction, and they were asked to sign an informed consent form if they agreed to complete the survey. During the survey, the interviewers were asked to read each question to the respondents and record the respondent’s answer in a standard format questionnaire. Respondents were ensured that their participation was voluntary and their response would be anonymous. The questionnaire took approximately 20 minutes to complete.

Data Collection

Data were gathered with the use of a face-to-face structured questionnaire interview during the month of October 2010 by professional reviewers at Millward Brown. It is a professorial market survey firm, and has affiliates in main cities in China. Thus, the survey were conducted in the eight cities at the same time, and completed in one week. With use of professional methodologies, the requested participants were approached with considering the balance among the main variables of city, gender, and age. The survey contained four sections. The questions were used for the selection of respondents in the first section and included the following criteria: (1) age between 14 and 60 years; (2) no one in the family holding a position in fields such as marketing research, media (TV station/broadcast/newspaper/magazine/Internet), advertising, or public relations; (3) no participation in any survey designed by a marketing research firm in the last three months; (4) have lived in the local city for at least one year. If the respondent candidates were not suitable for any requirements above, the following sections would be ended.

A total of 2,400 respondents who met the requirements were invited to answer the subsequent sections. The second section assessed the respondent’s prior Internet use experiences, including whether they had used the Internet within the last week and within last one month. Then, for those who acknowledged personal Internet use within the last week, questions were asked about the intensity of Internet use average time online for non-work purposes, both during the work week and on the weekends and activities they engaged in while online. The respondents were also asked to select their three favorite online activities. In the next section, the respondents again used the response of “Yes” or “No” to indicate whether they had ever engaged in common activities (e.g., shopping or visiting relatives or friends) during their leisure time within the last month. The final section was developed to establish other demographic characteristics (i.e., educational level, monthly income, etc.).

Statistical Analyses

All statistical analyses were performed using SPSS version 19.0. First, the respondents were divided into Internet users and non-Internet users and then descriptive statistics and Chi-square tests were applied to analyze the respondents’ demographic and socio-economic characteristics and Internet use. Based on a cluster analysis with respect to leisurely Internet use and online activities, the types of Internet dependence and use were identified. A logistic regression analysis was then used to estimate the odds of reporting Internet use. Finally, we again used logistic regression to adjust for common leisure activities to investigate the impact of Internet use on leisure activity engagement.

Demographic Characteristics of Internet Users

As shown in Table 1 , regarding Internet use, 66.2% ( n  = 1,589) of all respondents who reported that they had used the Internet within the past week were categorized as “Internet users”, and 29.2% ( n  = 700) of them who responded that they had not used the Internet within the past month were labeled as “non-Internet users.” Because only 4% ( n  = 111) of the respondents reported that they had accessed the Internet one week before during last month, this category of participants was not included in the subsequent analyses. Without adjustment for other variables, Internet use or the lack thereof varied significantly according to a series of demographic measures: gender, age, monthly salary, education level, occupation, and city ( χ 2 ≥14.6, p <0.001). The number of family members did not have a significant influence on users’ Internet usage ( χ 2  = 5.8, p >0.05). Additional details for main demographic characteristics are shown in Table 1 .

Patterns of Internet use

Overall, the most common use of the Internet may be for entertainment, including “watching online videos” (59.0% of respondents reported their engagement in this activity within the preceding week, and 30.0% considered it as one of their favorite online activities), “enjoying or downloading songs or movies” (52.9% engagement, and 24.0% reported it as a favorite activity), “playing online games” (40.7% engagement, and 24.6% reported it as a favorite activity), communications with the “use of chat tools or instant messaging” (59.1% engagement, and 39.5% reported the activity as a favorite), and “Email” (40.5% of engagement, and 16.4% of favorites), followed by information surfing activities such as “getting or reading news” (50.5% engagement, and 27.4% of favorites) and “using a search engine to find information” (33.4% engagement, and 12.1% of favorites) and transaction activities such as “buying or selling online” (27.6% engagement, and 9.1% of favorites).

According to [16] , Internet user typology reflects not only how different user groups use the Internet in various ways but also how dissimilar is the potential of user types to exploit the benefits of the Internet. To further understand the patterns of Internet use regarding online activities, we conducted a K-mean cluster score analysis to indentify user groups. K-means clustering is a one of methods of cluster analysis, which is the task of grouping a set of objects in such a way that objects in the same group (called cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).

In this cluster process, responses regarding engagement within the preceding week and favorite were considered together. In the raw data, participants responded “Yes” (coded as 1) or “No” (coded as 0) to engagement and favorite, respectively. First, the raw responses of engagement and favorite were summarized for each online activity. For example, one respondent reported her or his engagement in online videos within the preceding week, and also considered it as one of their favorite online activities, then the new response score is 2; if he or she only reported engagement or considered it as one of them, the new response scored as 1; or scored as 0 for two “No” response. Thus, all variables were within the same range of 0, 1, or 2, and we conducted a K-means score analysis of the transferred data. Then, to determine the number of clusters, we followed the procedures and results suggested by Brandtzæg and Karahasanovic [16] . As a result, except for non-Internet users, we identified four clusters denoting four Internet user types. Table 2 shows the mean score within each cluster. In terms of the user behavior typical of each cluster, we identified the following four types of Internet users. (1) Cluster 1: Advanced users (19.6% of the Internet users). In general, the mean scores of this user type are the highest for almost all Internet variables, indicating an extremely varied and broad Internet behavior. (2) Cluster 2: Informative or instrumental users (30.0% of the Internet users). The mean scores of this cluster are higher than those of the other clusters with regard to getting news. (3) Entertainment users (24.1% of the Internet users). These users have the highest mean scores in goal-oriented activities such as playing online games, watching online videos, and listening to online music. (4) Communication users (26.4% of the Internet users). These users were characterized by the most frequent use of chat tools or instant messaging.

The time spent on Internet use during leisure time can reflect the degree of Internet dependence. In this survey, Internet use was frequent in users’ leisure time; more than 96% of respondents reported going online in non-work situations. The percentage of respondents who reported less than four hours per a day was 65.5% and 45.9% on weekdays and weekends, respectively. A total of 26.5% of participants reported that they spent less than ten hours (including four or more hours) per day during weekday leisure time, whereas the percentage was higher on weekends (43.7%). A total of 4.0% and 6.9% of respondents reported ten or more hours of online time on weekdays and weekends, respectively. To describe Internet dependence, we again performed a K-means cluster analysis of the reported average time online per day to establish groups that varied in terms of online time spent in leisure time per day. Before performing this cluster analysis, we first transferred the categories of reported online time per day into absolute online time: 0 hours for “ = 0 h/D,” 2 hours for “(0, 4) h/D,” 7 hours for “(4, 10) h/D,” and 10 hours for “≥10 h/D.” After the transferred values of workdays and weekends were both entered as variables, we used the iterate and classify method and running means for new cluster centers for each iteration. With two clusters designed as the target, Internet users were classified as “regular Internet users” (73.8%, n  = 1173; the mode category of reported online time were (0, 4) h/D for both workday and weekend) or “heavy Internet users” (26.2%, n  = 416; the mode category of reported online time were (4, 10) h/D for both workday and weekend).

To help understand whether there is significant influence of demographic variables on Internet usage, we conducted a logistic regression analysis to investigate factors that may predict different types of users. Because the variable number of members in a household was not a significant factor for comparing Internet users and non-Internet users, it was not considered as one of the predictors in the regression models. Table 3 presents the results of the logistic regression analysis, explaining the predictor for the different user types. The particular type of user (i.e., non-user; the type reflecting the aims of Internet use as an advanced user, information user, enjoyment user, or communication user; or the classification reflecting the Internet dependence as a regular user or heavy user) was used as the dependent variable. The independent variables are listed as age, gender, income, education level, and city. During the logistic regressions analyses, non-Internet users’ reference group was Internet user (i.e., coded as 0); and as for one specific user type, all other Internet users were identified as the reference group (for example, information user, communication user, and enjoyment user were all coded as 0 when performing the logistic regressions for advanced users (coded as 1); in the same way, regular user was coded as 0 for estimating the effect of all independent factors on heavy user). For each user type, we report the results from the total sample and the Nagelkerke R 2 values, which provide an indication of the amount of variation in the dependent variable (user type) explained by the model (from a minimum value of 0 to a maximum of approximately 1). The “odds ratio” values below the independent variables column gives the factor by which the odds of a user belonging to a specific user type increase when the value of a predictor is increased by one code value. This statistic reflects the effect size and the direction of the relationship. As for non-Internet users, all of the independent variables contributed significantly. The model as a whole explained 49% of the variance in non-users. As shown in the table, belonging to an older age category increases the probability by a factor of 1.81. Being female increases the odds of being a non-user by a factor of 1.34. Increasing the income and education level by one code value decreases the probability by a factor of 0.76 or 0.58, respectively. The factor of city was also found as one of significant resources for estimating non-Internet user, the results may indicate that respondents live in lower developed cities tend to report no Internet use (odd rate = 1.10, and generally the developed level of Beijing, Shanghai, and Guangzhou is higher than other cities). As for the four types of Internet activities, all independent variables only account for 4.0% to 15% of the variance among the different user types. Being older increases the probability of being an information user (odd ratio = 1.32), whereas this factor decreases the probability of being one of the other three types of user (odd ratio ≤0.92). Females tend to be communication users more often than males do (odds ratio = 1.75), whereas males tend to be enjoyment users more than females do (odd ratio = 0.53). Increasing the income and education level increases the probability that users will be advanced users (odds ratios were 1.17 and 1.60, respectively), whereas these factors decrease the probability of being enjoyment users (odds ratios were 0.83 and 0.71, respectively). Respondents live in cities with lower developed level tend to be enjoyment user (odd ratio = 1.06), whereas this factor decrease the probability of advanced user (odd ratio = 0.86). As for the types used for categorizing Internet dependence, all independent variables only account for 5% of the variance among regular or heavy users. Being older decreases the probability of being a heavy user (odds ratios = 0.88), and those with a higher education level tend to be heavy users (odds ratios = 1.12).

Leisure Time Internet Dependence and Leisure Activity Engagement

To help understand whether there is a significant difference in leisure activity engagement between non-Internet users and Internet users, a logistic regression model was developed to investigate the predictive effect of leisure time Internet dependence (for non-Internet users, regular Internet users, and heavy Internet users) in terms of other demographic variables (i.e., age, gender, income, and education; since the differences of developed level were not considered carefully in the research design, the factor of city was not considered as one of the predictors in the regression models). As shown in Table 4 , each activity was identified as “physical activity,” “mental activity,” and “social activity”. Among all respondents, the five most popular leisure time activities are related to the mental activities: “watching TV” (80.5%), “reading the newspaper” (52.9%), “listening to music” (38.1%), “reading a magazine” (33.7%), and “going shopping” (27.5%). Regarding the effects of Internet dependence, increasing the leisure time dependence category by one code value increases the probability of engaging in three mental activities (i.e., “reading a magazine,” “going to the cinema,” and “going to an amusement park”; odds ratio ≥1.47) and in two social activities (i.e., “singing karaoke with friends,” and “going to a café or bar”; odds ratio ≥1.27). In contrast, Internet users were less engaged in physical exercise-related activities such as “playing sports/physical exercise for health” and “going to a park” (odds ratio ≤0.75). The variables of age, gender, and education level emerged as significant predictors for most leisure activities in all respondents. For more information regarding the differences among these demographic variables, see Table 4 .

Internet Use and Leisure Activities Engagements

With respect to the differences among Internet user types, a logistic regression model was first generated to compare leisure activity engagement between each type of Internet user (i.e., advanced user, information user, enjoyment user, and communication user) and non-Internet user, adjusting for all demographic variables (i.e., age, gender, income, city, and education). We then used the same method to compare leisure activity engagement among the four Internet user types. The results suggested that advanced Internet users were generally more active in leisure time activities than non-Internet users and other user types. In terms of reading books, dining in restaurants, and visiting relatives or friends/joining a party, advanced users reported more engagement than other user types (AOR ≥1.85, p <0.01), and no significant differences were observed for engagement in these activities between each of the other three user types and non-users. In contrast, there was no significant difference between advanced Internet users and the other three user types and non-users in the activities of “watching TV”; “seeing a play, show, or drama”; and “playing sports/physical exercise for health.” For the activities of “going to the cinema,” “going shopping,” “going to an amusement park,” “going to a park,” there were no significant differences found between advanced users and enjoyment users. Unlike other activities, the finding also demonstrated that information users were less active in singing karaoke with friends than the other three Internet user types. Additional results are shown in Table 5 .

Respondent Characteristics and Internet Usage Pattern

In the current study, the main aim was to understand factors and patterns associated with Internet use and their impact on users’ leisure time activities among an urban population in China. More than 66% of the participants answered that they had accessed the Internet in the last week; these respondents were labeled as “Internet users” in this study. Together with those who have not used the Internet within the past week but who have within the past month, the rate of Internet use reached 70% among urban citizens in China. Overall, the results were consistent with the latest CNNIC report, which indicated that 72.4% of urban Chinese people have accessed the Internet in the last six months [7] . Therefore, from a collective perspective, it is reasonable to characterize those who have used the Internet within the past week and those who have not accessed Internet within the past month as “Internet users” and “non-Internet users,” respectively. Investigating how many people use the Internet is very important for understanding the new digital divide, as Brandtzæg et al. addressed in their study [16] . In Europe, a recent survey conducted to understand this issue found that 60% of the population was identified to be either non-users (42%) or sporadic users (18%) [16] . In the US, the Pew Internet and American Life Project represent one of the largest efforts to gather large-scale data on Internet use. This project uses nationwide telephone surveys, most recently in December 2008 (N = 2253). Internet penetration reached 74% for all American adults in 2008, reflecting a sharp increase from 66% 3 years earlier [11] . Compared with these results surveyed in other countries, the digital divide seems smaller in China’s urban population. However, the largest digital divide in China may emerge between urban and rural populations, where the percentage of Internet usage was 72.4% and 27.6%, respectively [7] .

Regarding the pattern of the Internet, of particular concern is the proportion of those who engage in different online activities. The number of potential functions of the Internet is substantial, and the activities are diverse. In our study, instant messaging and online video watching are the most prevalent, with nearly 60% of Chinese citizens engaging in these two activities when they access the Internet; over half of our participants reported that they engaged in downloading songs or movies and getting or reading news. Together with the CCNIC report, email, Internet games, searching for information and blogging are currently popular in China [7] . However, there are significant differences in engagement in some online activities between current respondents and other populations. For example, the use of chat tools or instant messaging, email, blogging, and the use of SNS sites have gained ground in communication activities in Chinese citizens. In China, instant messaging remains the most popular Internet activity, and less than 50% of Internet users reported that they use email to contact others. In contrast, in the US, over 80% of online users send and receive email, making email the most popular online activity [11] . With respect to Internet dependence among Internet users, overall, the time of Internet use reported in this survey is higher during weekend leisure time than during workday leisure time. In this survey, we did not identify the purpose of Internet use during leisure time, and most Internet users reported a reasonable time spent online. One successful approach to understanding Internet use patterns is to identify Internet user types [16] . Considering both the responses for activity engagement and favorite activities, we were able to successfully identify five user types: non-users, advanced users, information users, enjoyment users, and communication users. Unlike the term used in a previous study [16] , we used the term “communication users” instead of “sporadic users” because email or instant messaging was very popular among our respondents. We used the term “information users” to label information-oriented activities such as getting or reading news and searching for information. By clustering the users’ time spent on Internet use during their leisure time, we also identified three user types to describe their Internet dependence: Non-Users, Regular Users, and Heavy Users. Together with the results of previous studies [12] , [16] , this categorization is a very useful method by which to distinguish user types for analysis purposes. The two studies also suggested that there may be some differences in terms of the frequency of activity engagement among different survey samples; therefore, it is reasonable and important to use corresponding words to label user types.

A large body of studies suggests that the Internet means different things to different people and is used in different ways for different purposes. A number of factors have been found to relate to Internet access and use, including socioeconomic variables, demographic variables, and education [23] . With regard to the demographic groups, the present results support previous findings (e.g., younger groups are more likely to use the Internet, and the proportion of those who reported that they had not accessed the Internet within the past week decreases with age). The same effect of age on Internet usage was found in the US. The latest figures from adults in a nationally representative sample of US adults showed that 30% of people ages 18–32 use the Internet in comparison with 24% of three other generations of ages 55–63, 64–72, and 73+ [11] . The web continues to be populated largely by younger generations. The “gender gap” in Internet access has been found in a number of previous investigations [23] . The results showed that a greater proportion of Chinese male citizens (70%) accessed the Internet than females (63%). The digital pattern of Internet use is also shaped by socioeconomic status and education. The results from this study show that the prevalence of Internet use in populations with higher economic and education levels is high. Fully 80% of those in higher income brackets (over 5,000 CNY monthly) in major Chinese cities have Internet access, compared with 62% of adults who have lower incomes. In terms of education, more than 80% of Chinese citizens who have higher levels of education (i.e., associate degree or above) are identified as active Internet users in comparison with less than 50% of those with middle or lower level education. These results were consistent with Pew findings [11] , which indicate that Internet usage is also relatively well represented across most income and education brackets, although usage increases in relation to annual income and education. Some 95% of Americans who live in households earning $75,000 or more a year use the Internet at least occasionally, compared with 70% of those living in households earning less than $75,000 [24] . In US, the Internet use level is much higher for individuals with a higher level of education (i.e., some college or above) than that for those with a mid-level education (i.e., high school or below) [25] . With consistent results in other populations in developed countries such as the US, the socioeconomic divide with regard to digital access is not likely to close quickly in China, especially among rural citizens.

When different user types are taken into consideration, these digital gaps are changed. For example, older people tend to be information users more often than other user types. The predicting effect of gender is only significant for enjoyment users and communication users. The overall picture is that more males than females tend to be enjoyment users, whereas more females than males tend to be communication users. Those with a higher income and education level tend to be advanced users, whereas those with a lower income and education level tend to be enjoyment users. Regarding time spent on leisure time Internet use, younger users tend to be more dependent on the Internet than older users, and those with higher income and education level also tend to be heavy Internet users more frequently than those with lower income. There is growing evidence that the digital divide in access in terms of gender is closing or has closed as more women begin to use the Internet [26] , [27] ; however, the gender gap in Internet usage is still present, especially among different user types.

Internet Use and Engagement in Leisure Activities

In this study, our other main aim was to understand the effect of user leisure time, Internet dependence, and Internet user types on users’ leisure activities. We found that Internet dependence neither decreased nor increased engagement in some mental activities (e.g., watching TV, listening to the radio, reading the newspaper, and going shopping), socially directed activities (e.g., visiting relatives or friends/joining a party and playing chess, cards, or mahjong), and the physical activity of going on excursions or going camping. In contrast, those with higher Internet dependence tend to be more active in interacting with others by singing karaoke and going to a bar or café than non-Internet users and tend to be more engaged in personal promotion or mental activities such as reading a magazine, going to the cinema, and going to an amusement park. Generally, when investigating the effect of Internet use on a life style, these activities could be considered as important indications of the positive impact of personal or social leisure time activities. Consistent with some previous studies [14] , [18] , [19] , [20] , our study tends to support the argument that Internet usage contributes to maintaining or increasing many aspects of a citizen’s mental and social activity engagements as the degree of Internet dependence increases. However, Internet users reported less engagement in physical activities such as playing sports/physical exercise for health than non-Internet users did. Jerome and McAuley’s study suggests that efforts to increase personal efficacy in overcoming barriers to exercise may be more practical and have a greater impact on physical activity levels than trying to decrease leisure Internet use, especially among adults [9] .

In addition to efforts to investigate the relationship between leisure activities and time spent involved in leisurely Internet use, an examination of the association between leisure activity and the type of Internet use may prove to be more illustrative. In this study, heavy Internet users tended to be less engaged in going to a park than non-Internet users, but the difference was not significant between advanced users and non-Internet users. In addition, the significant difference in the engagement of going to excursions/going camping was only found between information users and non-Internet users. Compared with non-Internet users, time spent on leisure Internet use was not a significant factor for predicting listening to music; however, more advanced users, enjoyment users, and communication users reported more engagement in this activity. Unlike other types of Internet user, more information users tend to be less engaged in the social activities of singing karaoke with friends and going to a café or bar. The reason for this trend may be that older respondents tend to be information users more often than younger respondents do, and engagement in these two leisure activities decreases as age increases. Overall, the current results indicate that advanced users tend to be more active in both regular leisure activities and Internet activities, and in some sense, these engagements are independent of time spent on Internet use. This study provides evidence supporting the importance of identifying Internet user types to investigate the pattern of Internet usage and its impact on respondent’s leisure activities.

Limitations

Our results should be interpreted with several limitations in mind. First, like many previous studies, a convenience sample was used in this study to recruit respondents. Although we tried to balance the participants in terms of gender, age group, and city, it is very difficult to balance respondents in terms of income level, education level, and occupation. In addition, these conclusions should be understood not to apply to all of China’s subpopulations because there are digital divides regarding Internet use and leisure activities between different groups in China. Especially in accepting the findings regarding city difference should be cautious, since the developed level between the cities were not considered carefully in the research design. Second, there will be some interaction effects in terms of Internet use and leisure activities among types of Internet use, time spent on leisure Internet use (i.e., Internet leisure dependence), and demographics. Third, although we have attempted to identify social, mental, and physical types of leisure activity, each leisure activity actually has social, mental, and physical functions of varying degrees. Further studies may attempt to identify the corresponding role of each leisure activity to improve the systematic understanding of associations between Internet usage and leisure activity.

Conclusions

In conclusion, the current study indicates the following: 1) Internet use is one of very common leisure activities in Chinese urban citizens; and age, gender, income level, and education level are the key important factors that affect Internet access. 2) Overall Internet usage has different impacts on leisure activity engagement according to the specific type of leisure activity. High Internet dependence has no significant negative influence on mental or social activity engagement, but heavy Internet users tend to be less in engaged in physical activities than non-Internet users. 3) Our study describes an effective method by which to compare leisure activities among different types of Internet users and confirms the argument that Internet use means different things to different people.

Acknowledgments

We would like to thank academic editor and anonymous reviewers for their suggestions.

Funding Statement

This research was supported by the National Natural Science Foundation of China (NSFC, 31271100), the Hong Kong Scholars Program, and Panmedia Institute. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.