methods used in research papers

When you choose to publish with PLOS, your research makes an impact. Make your work accessible to all, without restrictions, and accelerate scientific discovery with options like preprints and published peer review that make your work more Open.

• PLOS Biology
• PLOS Climate
• PLOS Complex Systems
• PLOS Computational Biology
• PLOS Digital Health
• PLOS Genetics
• PLOS Global Public Health
• PLOS Medicine
• PLOS Mental Health
• PLOS Neglected Tropical Diseases
• PLOS Pathogens
• PLOS Sustainability and Transformation
• PLOS Collections
• How to Write Your Methods

Ensure understanding, reproducibility and replicability

What should you include in your methods section, and how much detail is appropriate?

Why Methods Matter

The methods section was once the most likely part of a paper to be unfairly abbreviated, overly summarized, or even relegated to hard-to-find sections of a publisher’s website. While some journals may responsibly include more detailed elements of methods in supplementary sections, the movement for increased reproducibility and rigor in science has reinstated the importance of the methods section. Methods are now viewed as a key element in establishing the credibility of the research being reported, alongside the open availability of data and results.

A clear methods section impacts editorial evaluation and readers’ understanding, and is also the backbone of transparency and replicability.

For example, the Reproducibility Project: Cancer Biology project set out in 2013 to replicate experiments from 50 high profile cancer papers, but revised their target to 18 papers once they understood how much methodological detail was not contained in the original papers.

What to include in your methods section

What you include in your methods sections depends on what field you are in and what experiments you are performing. However, the general principle in place at the majority of journals is summarized well by the guidelines at PLOS ONE : “The Materials and Methods section should provide enough detail to allow suitably skilled investigators to fully replicate your study. ” The emphases here are deliberate: the methods should enable readers to understand your paper, and replicate your study. However, there is no need to go into the level of detail that a lay-person would require—the focus is on the reader who is also trained in your field, with the suitable skills and knowledge to attempt a replication.

A constant principle of rigorous science

A methods section that enables other researchers to understand and replicate your results is a constant principle of rigorous, transparent, and Open Science. Aim to be thorough, even if a particular journal doesn’t require the same level of detail . Reproducibility is all of our responsibility. You cannot create any problems by exceeding a minimum standard of information. If a journal still has word-limits—either for the overall article or specific sections—and requires some methodological details to be in a supplemental section, that is OK as long as the extra details are searchable and findable .

Imagine replicating your own work, years in the future

As part of PLOS’ presentation on Reproducibility and Open Publishing (part of UCSF’s Reproducibility Series ) we recommend planning the level of detail in your methods section by imagining you are writing for your future self, replicating your own work. When you consider that you might be at a different institution, with different account logins, applications, resources, and access levels—you can help yourself imagine the level of specificity that you yourself would require to redo the exact experiment. Consider:

• Which details would you need to be reminded of? 
• Which cell line, or antibody, or software, or reagent did you use, and does it have a Research Resource ID (RRID) that you can cite?
• Which version of a questionnaire did you use in your survey? 
• Exactly which visual stimulus did you show participants, and is it publicly available? 
• What participants did you decide to exclude? 
• What process did you adjust, during your work? 

Tip: Be sure to capture any changes to your protocols

You yourself would want to know about any adjustments, if you ever replicate the work, so you can surmise that anyone else would want to as well. Even if a necessary adjustment you made was not ideal, transparency is the key to ensuring this is not regarded as an issue in the future. It is far better to transparently convey any non-optimal methods, or methodological constraints, than to conceal them, which could result in reproducibility or ethical issues downstream.

Visual aids for methods help when reading the whole paper

Consider whether a visual representation of your methods could be appropriate or aid understanding your process. A visual reference readers can easily return to, like a flow-diagram, decision-tree, or checklist, can help readers to better understand the complete article, not just the methods section.

Ethical Considerations

In addition to describing what you did, it is just as important to assure readers that you also followed all relevant ethical guidelines when conducting your research. While ethical standards and reporting guidelines are often presented in a separate section of a paper, ensure that your methods and protocols actually follow these guidelines. Read more about ethics .

Existing standards, checklists, guidelines, partners

While the level of detail contained in a methods section should be guided by the universal principles of rigorous science outlined above, various disciplines, fields, and projects have worked hard to design and develop consistent standards, guidelines, and tools to help with reporting all types of experiment. Below, you’ll find some of the key initiatives. Ensure you read the submission guidelines for the specific journal you are submitting to, in order to discover any further journal- or field-specific policies to follow, or initiatives/tools to utilize.

Tip: Keep your paper moving forward by providing the proper paperwork up front

Be sure to check the journal guidelines and provide the necessary documents with your manuscript submission. Collecting the necessary documentation can greatly slow the first round of peer review, or cause delays when you submit your revision.

Randomized Controlled Trials – CONSORT The Consolidated Standards of Reporting Trials (CONSORT) project covers various initiatives intended to prevent the problems of  inadequate reporting of randomized controlled trials. The primary initiative is an evidence-based minimum set of recommendations for reporting randomized trials known as the CONSORT Statement . 

Systematic Reviews and Meta-Analyses – PRISMA The Preferred Reporting Items for Systematic Reviews and Meta-Analyses ( PRISMA ) is an evidence-based minimum set of items focusing  on the reporting of  reviews evaluating randomized trials and other types of research.

Research using Animals – ARRIVE The Animal Research: Reporting of In Vivo Experiments ( ARRIVE ) guidelines encourage maximizing the information reported in research using animals thereby minimizing unnecessary studies. (Original study and proposal , and updated guidelines , in PLOS Biology .) 

Laboratory Protocols Protocols.io has developed a platform specifically for the sharing and updating of laboratory protocols , which are assigned their own DOI and can be linked from methods sections of papers to enhance reproducibility. Contextualize your protocol and improve discovery with an accompanying Lab Protocol article in PLOS ONE .

Consistent reporting of Materials, Design, and Analysis – the MDAR checklist A cross-publisher group of editors and experts have developed, tested, and rolled out a checklist to help establish and harmonize reporting standards in the Life Sciences . The checklist , which is available for use by authors to compile their methods, and editors/reviewers to check methods, establishes a minimum set of requirements in transparent reporting and is adaptable to any discipline within the Life Sciences, by covering a breadth of potentially relevant methodological items and considerations. If you are in the Life Sciences and writing up your methods section, try working through the MDAR checklist and see whether it helps you include all relevant details into your methods, and whether it reminded you of anything you might have missed otherwise.

Summary Writing tips

The main challenge you may find when writing your methods is keeping it readable AND covering all the details needed for reproducibility and replicability. While this is difficult, do not compromise on rigorous standards for credibility!

• Keep in mind future replicability, alongside understanding and readability.
• Follow checklists, and field- and journal-specific guidelines.
• Consider a commitment to rigorous and transparent science a personal responsibility, and not just adhering to journal guidelines.
• Establish whether there are persistent identifiers for any research resources you use that can be specifically cited in your methods section.
• Deposit your laboratory protocols in Protocols.io, establishing a permanent link to them. You can update your protocols later if you improve on them, as can future scientists who follow your protocols.
• Consider visual aids like flow-diagrams, lists, to help with reading other sections of the paper.
• Be specific about all decisions made during the experiments that someone reproducing your work would need to know.

Don’t

• Summarize or abbreviate methods without giving full details in a discoverable supplemental section.
• Presume you will always be able to remember how you performed the experiments, or have access to private or institutional notebooks and resources.
• Attempt to hide constraints or non-optimal decisions you had to make–transparency is the key to ensuring the credibility of your research.
• How to Write a Great Title
• How to Write an Abstract
• How to Report Statistics
• How to Write Discussions and Conclusions
• How to Edit Your Work

The contents of the Peer Review Center are also available as a live, interactive training session, complete with slides, talking points, and activities. …

The contents of the Writing Center are also available as a live, interactive training session, complete with slides, talking points, and activities. …

There’s a lot to consider when deciding where to submit your work. Learn how to choose a journal that will help your study reach its audience, while reflecting your values as a researcher…

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Dissertation
• What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

Published on 25 February 2019 by Shona McCombes . Revised on 10 October 2022.

Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation, or research paper, the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research.

It should include:

• The type of research you conducted
• How you collected and analysed your data
• Any tools or materials you used in the research
• Why you chose these methods
• Your methodology section should generally be written in the past tense .
• Academic style guides in your field may provide detailed guidelines on what to include for different types of studies.
• Your citation style might provide guidelines for your methodology section (e.g., an APA Style methods section ).

Instantly correct all language mistakes in your text

Be assured that you'll submit flawless writing. Upload your document to correct all your mistakes.

Table of contents

How to write a research methodology, why is a methods section important, step 1: explain your methodological approach, step 2: describe your data collection methods, step 3: describe your analysis method, step 4: evaluate and justify the methodological choices you made, tips for writing a strong methodology chapter, frequently asked questions about methodology.

The only proofreading tool specialized in correcting academic writing

The academic proofreading tool has been trained on 1000s of academic texts and by native English editors. Making it the most accurate and reliable proofreading tool for students.

Correct my document today

Your methods section is your opportunity to share how you conducted your research and why you chose the methods you chose. It’s also the place to show that your research was rigorously conducted and can be replicated .

It gives your research legitimacy and situates it within your field, and also gives your readers a place to refer to if they have any questions or critiques in other sections.

You can start by introducing your overall approach to your research. You have two options here.

Option 1: Start with your “what”

What research problem or question did you investigate?

• Aim to describe the characteristics of something?
• Explore an under-researched topic?
• Establish a causal relationship?

And what type of data did you need to achieve this aim?

• Quantitative data , qualitative data , or a mix of both?
• Primary data collected yourself, or secondary data collected by someone else?
• Experimental data gathered by controlling and manipulating variables, or descriptive data gathered via observations?

Option 2: Start with your “why”

Depending on your discipline, you can also start with a discussion of the rationale and assumptions underpinning your methodology. In other words, why did you choose these methods for your study?

• Why is this the best way to answer your research question?
• Is this a standard methodology in your field, or does it require justification?
• Were there any ethical considerations involved in your choices?
• What are the criteria for validity and reliability in this type of research ?

Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .

Quantitative methods

In order to be considered generalisable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.

Here, explain how you operationalised your concepts and measured your variables. Discuss your sampling method or inclusion/exclusion criteria, as well as any tools, procedures, and materials you used to gather your data.

Surveys Describe where, when, and how the survey was conducted.

• How did you design the questionnaire?
• What form did your questions take (e.g., multiple choice, Likert scale )?
• Were your surveys conducted in-person or virtually?
• What sampling method did you use to select participants?
• What was your sample size and response rate?

Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.

• How did you design the experiment ?
• How did you recruit participants?
• How did you manipulate and measure the variables ?
• What tools did you use?

Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.

• Where did you source the material?
• How was the data originally produced?
• What criteria did you use to select material (e.g., date range)?

The survey consisted of 5 multiple-choice questions and 10 questions measured on a 7-point Likert scale.

The goal was to collect survey responses from 350 customers visiting the fitness apparel company’s brick-and-mortar location in Boston on 4–8 July 2022, between 11:00 and 15:00.

Here, a customer was defined as a person who had purchased a product from the company on the day they took the survey. Participants were given 5 minutes to fill in the survey anonymously. In total, 408 customers responded, but not all surveys were fully completed. Due to this, 371 survey results were included in the analysis.

Qualitative methods

In qualitative research , methods are often more flexible and subjective. For this reason, it’s crucial to robustly explain the methodology choices you made.

Be sure to discuss the criteria you used to select your data, the context in which your research was conducted, and the role you played in collecting your data (e.g., were you an active participant, or a passive observer?)

Interviews or focus groups Describe where, when, and how the interviews were conducted.

• How did you find and select participants?
• How many participants took part?
• What form did the interviews take ( structured , semi-structured , or unstructured )?
• How long were the interviews?
• How were they recorded?

Participant observation Describe where, when, and how you conducted the observation or ethnography .

• What group or community did you observe? How long did you spend there?
• How did you gain access to this group? What role did you play in the community?
• How long did you spend conducting the research? Where was it located?
• How did you record your data (e.g., audiovisual recordings, note-taking)?

Existing data Explain how you selected case study materials for your analysis.

• What type of materials did you analyse?
• How did you select them?

In order to gain better insight into possibilities for future improvement of the fitness shop’s product range, semi-structured interviews were conducted with 8 returning customers.

Here, a returning customer was defined as someone who usually bought products at least twice a week from the store.

Surveys were used to select participants. Interviews were conducted in a small office next to the cash register and lasted approximately 20 minutes each. Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.

Mixed methods

Mixed methods research combines quantitative and qualitative approaches. If a standalone quantitative or qualitative study is insufficient to answer your research question, mixed methods may be a good fit for you.

Mixed methods are less common than standalone analyses, largely because they require a great deal of effort to pull off successfully. If you choose to pursue mixed methods, it’s especially important to robustly justify your methods here.

Prevent plagiarism, run a free check.

Next, you should indicate how you processed and analysed your data. Avoid going into too much detail: you should not start introducing or discussing any of your results at this stage.

In quantitative research , your analysis will be based on numbers. In your methods section, you can include:

• How you prepared the data before analysing it (e.g., checking for missing data , removing outliers , transforming variables)
• Which software you used (e.g., SPSS, Stata or R)
• Which statistical tests you used (e.g., two-tailed t test , simple linear regression )

In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).

Specific methods might include:

• Content analysis : Categorising and discussing the meaning of words, phrases and sentences
• Thematic analysis : Coding and closely examining the data to identify broad themes and patterns
• Discourse analysis : Studying communication and meaning in relation to their social context

Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process.

Above all, your methodology section should clearly make the case for why you chose the methods you did. This is especially true if you did not take the most standard approach to your topic. In this case, discuss why other methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.

In any case, it should be overwhelmingly clear to your reader that you set yourself up for success in terms of your methodology’s design. Show how your methods should lead to results that are valid and reliable, while leaving the analysis of the meaning, importance, and relevance of your results for your discussion section .

• Quantitative: Lab-based experiments cannot always accurately simulate real-life situations and behaviours, but they are effective for testing causal relationships between variables .
• Qualitative: Unstructured interviews usually produce results that cannot be generalised beyond the sample group , but they provide a more in-depth understanding of participants’ perceptions, motivations, and emotions.
• Mixed methods: Despite issues systematically comparing differing types of data, a solely quantitative study would not sufficiently incorporate the lived experience of each participant, while a solely qualitative study would be insufficiently generalisable.

Remember that your aim is not just to describe your methods, but to show how and why you applied them. Again, it’s critical to demonstrate that your research was rigorously conducted and can be replicated.

1. Focus on your objectives and research questions

The methodology section should clearly show why your methods suit your objectives  and convince the reader that you chose the best possible approach to answering your problem statement and research questions .

2. Cite relevant sources

Your methodology can be strengthened by referencing existing research in your field. This can help you to:

• Show that you followed established practice for your type of research
• Discuss how you decided on your approach by evaluating existing research
• Present a novel methodological approach to address a gap in the literature

3. Write for your audience

Consider how much information you need to give, and avoid getting too lengthy. If you are using methods that are standard for your discipline, you probably don’t need to give a lot of background or justification.

Regardless, your methodology should be a clear, well-structured text that makes an argument for your approach, not just a list of technical details and procedures.

Methodology refers to the overarching strategy and rationale of your research. Developing your methodology involves studying the research methods used in your field and the theories or principles that underpin them, in order to choose the approach that best matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. interviews, experiments , surveys , statistical tests ).

In a dissertation or scientific paper, the methodology chapter or methods section comes after the introduction and before the results , discussion and conclusion .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2022, October 10). What Is a Research Methodology? | Steps & Tips. Scribbr. Retrieved 31 May 2024, from https://www.scribbr.co.uk/thesis-dissertation/methodology/

Is this article helpful?

Shona McCombes

Other students also liked, how to write a dissertation proposal | a step-by-step guide, what is a literature review | guide, template, & examples, what is a theoretical framework | a step-by-step guide.

Get science-backed answers as you write with Paperpal's Research feature

What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Why is research methodology important?

Having a good research methodology in place has the following advantages: 3

• Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
• You can easily answer any questions about your research if they arise at a later stage.
• A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
• It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
• A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
• It also helps ensure that ethical guidelines are followed while conducting research.
• A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

Writing the methods section of a research paper? Let Paperpal help you achieve perfection

Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

• Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
• Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
• Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

• Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

• Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
• Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
• Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
• Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
• Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
• Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
• Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

• One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
• Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
• Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
• Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

• Sampling: The most common type is probability sampling.
• Interviews: Commonly telephonic or done in-person.
• Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
• Document review: Reviewing existing research or documents to collect evidence for supporting the research.
• Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

Let Paperpal help you write the perfect research methods section. Start now!

What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

• Measures of frequency (count, percent, frequency)
• Measures of central tendency (mean, median, mode)
• Measures of dispersion or variation (range, variance, standard deviation)
• Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

• Correlation: To understand the relationship between two or more variables.
• Cross-tabulation: Analyze the relationship between multiple variables.
• Regression analysis: Study the impact of independent variables on the dependent variable.
• Frequency tables: To understand the frequency of data.
• Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

• Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
• Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
• Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
• Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
• Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

• Research objectives, aims, and questions —these would help structure the research design.
• Review existing literature to identify any gaps in knowledge.
• Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
• Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
• Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

Got writer’s block? Kickstart your research paper writing with Paperpal now!

How to write a research methodology .

A research methodology should include the following components: 3,9

• Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
• Research method —this can be quantitative, qualitative, or mixed-method.
• Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
• Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
• Sampling —this involves selecting a representative subset of the population being studied.
• Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
• Data analysis —describe the data analysis methods you will use once you’ve collected the data.
• Research limitations —mention any limitations you foresee while conducting your research.
• Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
• Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

Streamline Your Research Paper Writing Process with Paperpal

The methods section is a critical part of the research papers, allowing researchers to use this to understand your findings and replicate your work when pursuing their own research. However, it is usually also the most difficult section to write. This is where Paperpal can help you overcome the writer’s block and create the first draft in minutes with Paperpal Copilot, its secure generative AI feature suite.  

With Paperpal you can get research advice, write and refine your work, rephrase and verify the writing, and ensure submission readiness, all in one place. Here’s how you can use Paperpal to develop the first draft of your methods section.  

• Generate an outline: Input some details about your research to instantly generate an outline for your methods section 
• Develop the section: Use the outline and suggested sentence templates to expand your ideas and develop the first draft.  
• P araph ras e and trim : Get clear, concise academic text with paraphrasing that conveys your work effectively and word reduction to fix redundancies. 
• Choose the right words: Enhance text by choosing contextual synonyms based on how the words have been used in previously published work.  
• Check and verify text : Make sure the generated text showcases your methods correctly, has all the right citations, and is original and authentic. .   

You can repeat this process to develop each section of your research manuscript, including the title, abstract and keywords. Ready to write your research papers faster, better, and without the stress? Sign up for Paperpal and start writing today!

Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

• Research design
• Data collection procedures
• Data analysis methods
• Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

• Participants should not be subjected to harm.
• Respect for the dignity of participants should be prioritized.
• Full consent should be obtained from participants before the study.
• Participants’ privacy should be ensured.
• Confidentiality of the research data should be ensured.
• Anonymity of individuals and organizations participating in the research should be maintained.
• The aims and objectives of the research should not be exaggerated.
• Affiliations, sources of funding, and any possible conflicts of interest should be declared.
• Communication in relation to the research should be honest and transparent.
• Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

Accelerate your research paper writing with Paperpal. Try for free now!

• Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
• Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
• The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
• Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
• What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
• What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
• Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
• Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
• What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
• Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

Paperpal is a comprehensive AI writing toolkit that helps students and researchers achieve 2x the writing in half the time. It leverages 21+ years of STM experience and insights from millions of research articles to provide in-depth academic writing, language editing, and submission readiness support to help you write better, faster.  

Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

Experience the future of academic writing – Sign up to Paperpal and start writing for free!  

Related Reads:

• Dangling Modifiers and How to Avoid Them in Your Writing 
• Webinar: How to Use Generative AI Tools Ethically in Your Academic Writing
• Research Outlines: How to Write An Introduction Section in Minutes with Paperpal Copilot
• How to Paraphrase Research Papers Effectively

Language and Grammar Rules for Academic Writing

Climatic vs. climactic: difference and examples, you may also like, mla works cited page: format, template & examples, how to ace grant writing for research funding..., powerful academic phrases to improve your essay writing , how to write a high-quality conference paper, how paperpal is enhancing academic productivity and accelerating..., academic editing: how to self-edit academic text with..., 4 ways paperpal encourages responsible writing with ai, what are scholarly sources and where can you..., how to write a hypothesis types and examples , what is academic writing: tips for students.

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Quantitative Methods
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques . Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS . 2nd edition. London: SAGE Publications, 2010.

Need Help Locating Statistics?

Resources for locating data and statistics can be found here:

Statistics & Data Research Guide

Characteristics of Quantitative Research

Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.

Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner].

Its main characteristics are :

• The data is usually gathered using structured research instruments.
• The results are based on larger sample sizes that are representative of the population.
• The research study can usually be replicated or repeated, given its high reliability.
• Researcher has a clearly defined research question to which objective answers are sought.
• All aspects of the study are carefully designed before data is collected.
• Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
• Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
• Researcher uses tools, such as questionnaires or computer software, to collect numerical data.

The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.

  Things to keep in mind when reporting the results of a study using quantitative methods :

• Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this section.
• Report unanticipated events that occurred during your data collection. Explain how the actual analysis differs from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
• Explain the techniques you used to "clean" your data set.
• Choose a minimally sufficient statistical procedure ; provide a rationale for its use and a reference for it. Specify any computer programs used.
• Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
• When using inferential statistics , provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level [report the actual p value].
• Avoid inferring causality , particularly in nonrandomized designs or without further experimentation.
• Use tables to provide exact values ; use figures to convey global effects. Keep figures small in size; include graphic representations of confidence intervals whenever possible.
• Always tell the reader what to look for in tables and figures .

NOTE:   When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing data does not undermine the validity of your final analysis.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Quantitative Research Methods. Writing@CSU. Colorado State University; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Basic Research Design for Quantitative Studies

Before designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables. Introduction The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information:

• Identifies the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
• Reviews the literature -- review scholarship on the topic, synthesizing key themes and, if necessary, noting studies that have used similar methods of inquiry and analysis. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge.
• Describes the theoretical framework -- provide an outline of the theory or hypothesis underpinning your study. If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e.g., historical, cultural, economic, etc.].

Methodology The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.

• Study population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded. Note the procedures used for their selection;
• Data collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i.e., government data] or you gathered it yourself. If you gathered it yourself, describe what type of instrument you used and why. Note that no data set is perfect--describe any limitations in methods of gathering data.
• Data analysis -- describe the procedures for processing and analyzing the data. If appropriate, describe the specific instruments of analysis used to study each research objective, including mathematical techniques and the type of computer software used to manipulate the data.

Results The finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Make sure that non-textual elements do not stand in isolation from the text but are being used to supplement the overall description of the results and to help clarify key points being made. Further information about how to effectively present data using charts and graphs can be found here .

• Statistical analysis -- how did you analyze the data? What were the key findings from the data? The findings should be present in a logical, sequential order. Describe but do not interpret these trends or negative results; save that for the discussion section. The results should be presented in the past tense.

Discussion Discussions should be analytic, logical, and comprehensive. The discussion should meld together your findings in relation to those identified in the literature review, and placed within the context of the theoretical framework underpinning the study. The discussion should be presented in the present tense.

• Interpretation of results -- reiterate the research problem being investigated and compare and contrast the findings with the research questions underlying the study. Did they affirm predicted outcomes or did the data refute it?
• Description of trends, comparison of groups, or relationships among variables -- describe any trends that emerged from your analysis and explain all unanticipated and statistical insignificant findings.
• Discussion of implications – what is the meaning of your results? Highlight key findings based on the overall results and note findings that you believe are important. How have the results helped fill gaps in understanding the research problem?
• Limitations -- describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the results.

Conclusion End your study by to summarizing the topic and provide a final comment and assessment of the study.

• Summary of findings – synthesize the answers to your research questions. Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the study.
• Recommendations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in practice.
• Future research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your study.

Black, Thomas R. Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics . London: Sage, 1999; Gay,L. R. and Peter Airasain. Educational Research: Competencies for Analysis and Applications . 7th edition. Upper Saddle River, NJ: Merril Prentice Hall, 2003; Hector, Anestine. An Overview of Quantitative Research in Composition and TESOL . Department of English, Indiana University of Pennsylvania; Hopkins, Will G. “Quantitative Research Design.” Sportscience 4, 1 (2000); "A Strategy for Writing Up Research Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper." Department of Biology. Bates College; Nenty, H. Johnson. "Writing a Quantitative Research Thesis." International Journal of Educational Science 1 (2009): 19-32; Ouyang, Ronghua (John). Basic Inquiry of Quantitative Research . Kennesaw State University.

Strengths of Using Quantitative Methods

Quantitative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships identified.

Among the specific strengths of using quantitative methods to study social science research problems:

• Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results;
• Allows for greater objectivity and accuracy of results. Generally, quantitative methods are designed to provide summaries of data that support generalizations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability;
• Applying well established standards means that the research can be replicated, and then analyzed and compared with similar studies;
• You can summarize vast sources of information and make comparisons across categories and over time; and,
• Personal bias can be avoided by keeping a 'distance' from participating subjects and using accepted computational techniques .

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Limitations of Using Quantitative Methods

Quantitative methods presume to have an objective approach to studying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior. As a consequence, the results of quantitative research may be statistically significant but are often humanly insignificant.

Some specific limitations associated with using quantitative methods to study research problems in the social sciences include:

• Quantitative data is more efficient and able to test hypotheses, but may miss contextual detail;
• Uses a static and rigid approach and so employs an inflexible process of discovery;
• The development of standard questions by researchers can lead to "structural bias" and false representation, where the data actually reflects the view of the researcher instead of the participating subject;
• Results provide less detail on behavior, attitudes, and motivation;
• Researcher may collect a much narrower and sometimes superficial dataset;
• Results are limited as they provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of human perception;
• The research is often carried out in an unnatural, artificial environment so that a level of control can be applied to the exercise. This level of control might not normally be in place in the real world thus yielding "laboratory results" as opposed to "real world results"; and,
• Preset answers will not necessarily reflect how people really feel about a subject and, in some cases, might just be the closest match to the preconceived hypothesis.

Research Tip

Finding Examples of How to Apply Different Types of Research Methods

SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.

SAGE Research Methods Online and Cases

• << Previous: Qualitative Methods
• Next: Insiderness >>
• Last Updated: May 30, 2024 9:38 AM
• URL: https://libguides.usc.edu/writingguide

How to Write the Methods Section of a Scientific Article

What Is the Methods Section of a Research Paper?

The Methods section of a research article includes an explanation of the procedures used to conduct the experiment. For authors of scientific research papers, the objective is to present their findings clearly and concisely and to provide enough information so that the experiment can be duplicated.

Research articles contain very specific sections, usually dictated by either the target journal or specific style guides. For example, in the social and behavioral sciences, the American Psychological Association (APA) style guide is used to gather information on how the manuscript should be arranged . As with most styles, APA’s objectives are to ensure that manuscripts are written with minimum distractions to the reader. Every research article should include a detailed Methods section after the Introduction.

Why is the Methods Section Important?

The Methods section (also referred to as “Materials and Methods”) is important because it provides the reader enough information to judge whether the study is valid and reproducible.

Structure of the Methods Section in a Research Paper

While designing a research study, authors typically decide on the key points that they’re trying to prove or the “ cause-and-effect relationship ” between objects of the study. Very simply, the study is designed to meet the objective. According to APA, a Methods section comprises of the following three subsections: participants, apparatus, and procedure.

How do You Write a Method Section in Biology?

In biological sciences, the Methods section might be more detailed, but the objectives are the same—to present the study clearly and concisely so that it is understandable and can be duplicated.

If animals (including human subjects) were used in the study, authors should ensure to include statements that they were treated according to the protocols outlined to ensure that treatment is as humane as possible.

• The Declaration of Helsinki is a set of ethical principles developed by The World Medical Association to provide guidance to scientists and physicians in medical research involving human subjects.

Research conducted at an institution using human participants is overseen by the Institutional Review Board (IRB) with which it is affiliated. IRB is an administrative body whose purpose is to protect the rights and welfare of human subjects during their participation in the study.

Literature Search

Literature searches are performed to gather as much information as relevant from previous studies. They are important for providing evidence on the topic and help validate the research. Most are accomplished using keywords or phrases to search relevant databases. For example, both MEDLINE and PubMed provide information on biomedical literature. Google Scholar, according to APA, is “one of the best sources available to an individual beginning a literature search.” APA also suggests using PsycINFO and refers to it as “the premier database for locating articles in psychological science and related literature.”

Authors must make sure to have a set of keywords (usually taken from the objective statement) to stay focused and to avoid having the search move far from the original objective. Authors will benefit by setting limiting parameters, such as date ranges, and avoiding getting pulled into the trap of using non-valid resources, such as social media, conversations with people in the same discipline, or similar non-valid sources, as references.

Related: Ready with your methods section and looking forward to manuscript submission ? Check these journal selection guidelines now!

What Should be Included in the Methods Section of a Research Paper?

One commonly misused term in research papers is “methodology.” Methodology refers to a branch of the Philosophy of Science which deals with scientific methods, not to the methods themselves, so authors should avoid using it. Here is the list of main subsections that should be included in the Methods section of a research paper ; authors might use subheadings more clearly to describe their research.

• Literature search : Authors should cite any sources that helped with their choice of methods. Authors should indicate timeframes of past studies and their particular parameters.
• Study participants : Authors should cite the source from where they received any non-human subjects. The number of animals used, the ages, sex, their initial conditions, and how they were housed and cared for, should be listed. In case of human subjects, authors should provide the characteristics, such as geographical location; their age ranges, sex, and medical history (if relevant); and the number of subjects. In case hospital records were used, authors should include the subjects’ basic health information and vital statistics at the beginning of the study. Authors should also state that written informed consent was provided by each subject.
• Inclusion/exclusion criteria : Authors should describe their inclusion and exclusion criteria, how they were determined, and how many subjects were eliminated.
• Group characteristics (could be combined with “Study participants”) : Authors should describe how the chosen group was divided into subgroups and their characteristics, including the control. Authors should also describe any specific equipment used, such as housing needs and feed (usually for animal studies). If patient records are reviewed and assessed, authors should mention whether the reviewers were blinded to them.
• Procedures : Authors should describe their study design. Any necessary preparations (e.g., tissue samples, drugs) and instruments must be explained. Authors should describe how the subjects were “ manipulated to answer the experimental question .” Timeframes should be included to ensure that the procedures are clear (e.g., “Rats were given XX drug for 14 d”). For animals sacrificed, the methods used and the protocols followed should be outlined.
• Statistical analyses: The type of data, how they were measured, and which statistical tests were performed, should be described. (Note: This is not the “results” section; any relevant tables and figures should be referenced later.) Specific software used must be cited.

What Should not be Included in Your Methods Section?

Common pitfalls can make the manuscript cumbersome to read or might make the readers question the validity of the research. The University of Southern California provides some guidelines .

• Background information that is not helpful must be avoided.
• Authors must avoid providing a lot of detail.
• Authors should focus more on how their method was used to meet their objective and less on mechanics .
• Any obstacles faced and how they were overcome should be described (often in your “Study Limitations”). This will help validate the results.

According to the University of Richmond , authors must avoid including extensive details or an exhaustive list of equipment that have been used as readers could quickly lose attention. These unnecessary details add nothing to validate the research and do not help the reader understand how the objective was satisfied. A well-thought-out Methods section is one of the most important parts of the manuscript. Authors must make a note to always prepare a draft that lists all parts, allow others to review it, and revise it to remove any superfluous information.

m so confused about ma research but now m okay so thank uh so mxh

Mil gracias por su ayuda.

Rate this article Cancel Reply

Your email address will not be published.

Enago Academy's Most Popular Articles

• AI in Academia
• Infographic
• Manuscripts & Grants
• Reporting Research
• Trending Now

Can AI Tools Prepare a Research Manuscript From Scratch? — A comprehensive guide

As technology continues to advance, the question of whether artificial intelligence (AI) tools can prepare…

Abstract Vs. Introduction — Do you know the difference?

Ross wants to publish his research. Feeling positive about his research outcomes, he begins to…

• Old Webinars
• Webinar Mobile App

Demystifying Research Methodology With Field Experts

Choosing research methodology Research design and methodology Evidence-based research approach How RAxter can assist researchers

• Manuscript Preparation
• Publishing Research

How to Choose Best Research Methodology for Your Study

Successful research conduction requires proper planning and execution. While there are multiple reasons and aspects…

Top 5 Key Differences Between Methods and Methodology

While burning the midnight oil during literature review, most researchers do not realize that the…

How to Draft the Acknowledgment Section of a Manuscript

Discussion Vs. Conclusion: Know the Difference Before Drafting Manuscripts

Sign-up to read more

Subscribe for free to get unrestricted access to all our resources on research writing and academic publishing including:

• 2000+ blog articles
• 50+ Webinars
• 10+ Expert podcasts
• 50+ Infographics
• 10+ Checklists
• Research Guides

We hate spam too. We promise to protect your privacy and never spam you.

I am looking for Editing/ Proofreading services for my manuscript Tentative date of next journal submission:

As a researcher, what do you consider most when choosing an image manipulation detector?

• Skip to primary navigation
• Skip to main content
• Skip to primary sidebar
• Email [email protected]

What Are the Different Types of Research Methods Used in Dissertation?

May 29, 2024 by Avery Devereaux

Introduction

Are you writing a research paper? Or are you beginning to write a dissertation? You need to know about the accolades of writing a dissertation.

One of them is different types of research methods. It will help you gain knowledge and interpret and write to delve better into the research topic. Therefore, it is important to know about different types of research, which will help you collect data properly and act on it.

Consequently, it is important to understand different research methodologies, which can help you write high-quality research papers. So, let’s learn more in the next section.

Different Types of Research Methods

After determining the hypothesis, you need to know different research methods. This will help you write an analysis much better. You will also be able to give direction to your dissertation paper and improve its quality.

Here are a few different types of research methods, as per the best –

Quantitative Research

Quantitative research is one of the primitive forms of scientific research. Following science, every other discipline followed the methods where numbers and facts acted to prove the hypothesis. The researchers collected primary data to give justice to your research topics and the objectives you have set.

However, to use the research method properly, you need to collect a large amount of data, which will help you to get accurate data. Therefore, with the right process, you can earn major benefits from the research method. So, if you are doing random sampling, you can use quantitative research to get the best outcome for your hypothesis.

Qualitative Research

As the humanities got more and more modern, the need for quantitative research increased. It was invented to get more human response from the respondents. The research method is much more humane and subjective in nature to put a better understanding upfront.

It mainly came into action after the birth of social psychology, which sought to understand human actions and usage.

For this, you can do observations, focus group discussions, interviews, and historical research. This will help you understand more about human interactions and their relation to social surroundings. Therefore, qualitative studies came into play to get a better understanding of humans.

Mixed Methods

Another addition to the list is mixed methods. It is a modern approach taken up by social science departments that seek to use quantitative and qualitative data to prove the relevancy of their data and hypothesis. That’s why most professors today ask researchers to use mixed methods to give a better understanding of the topic.

The modern approach has been fruitful, as numbers paint the picture for the holistic understanding to follow and coincide with it. That way, you get better results and properly analyze the subject. Also, it will increase the chance of error in your paper and follow the principles of falsification.

Fundamental Research

After moving away from the social science approach, let’s take the scientific approach, which aims to decipher what’s happening and formulate a theory. This approach is applicable to subjects like Math and psychology , where you can solve equations or behavioral patterns to present a theoretical understanding.

Moreover, fundamental research mostly applies to psychology, as it helps companies or institutions with their staff behavior. They study the behavorial pattern to come up with useful insights. That way, you will see in some cases, economics disciplines use the approach to offer an understanding of the correlation between two principles.

Conceptual Research

Another research method is conceptual research. In this research, philosophers mostly take abstract ideas and build theories around them. Here, there is no need for practical research, as it surrounds the present situations or visits past concepts and puts forth their discussion on it.

The methodology works by observing and understanding the situation in your own terms and interpreting them factually. The philosophers used the concept to understand the environment, nature, and everyday situations and forward a commentary of theories and inferences. Moreover, it enables thinkers to have multiple theories and inferences regarding one situation and revisit them or analyze the present ones to create new theories.

Empirical Research

Often, two variables affect each other to create a sense of mutual understanding. For example, if the inflation rate rises, your purchasing power decreases due to the high cost of prices and mortgage rates. Consequently, when you try to study the events it leads to experiments and observations, which is in other empirical research.

You will mostly see the usage of empirical research in fundamental sciences like chemistry and Physics. Those disciplines show how variables affect each other and lead to the creation of a world of their own. For this, you need to generate a hypothesis and work your research around to prove it null or alternative.

Descriptive Research

We all have read Max Weber, who suggests that social scientists should discuss facts and situations as they are without revealing their understanding. Similarly, this principle applies to descriptive research , where you have to discuss the variables as they are without actually controlling them.

This is a type of research in which you discuss the facts of the situation rather than putting your analysis in it. Therefore, it is also known as Ex post facto research. Research methods are mostly used when researchers have to describe characteristics, frequencies, and trends.

The Bottom Line

In the end, now that you know different types of research methods, you can choose any of them and apply them in your research. However, you can ask us, Ivory Research, to do the job for you and write you a great dissertation paper.

Here are a few frequently asked questions –

What are the research methods?

Research methods are the process through which research is conducted to understand its relevancy and understanding in the modern world.

What is a research method example?

Surveys, observations, interviews, focus group discussions, and so on are a few examples of research methods.

What is the concept of research?

The creation of new knowledge or creating a new concept around the existing one is a concept of research. 

Buy Me A Coffee

The Havok Journal seeks to serve as a voice of the Veteran and First Responder communities through a focus on current affairs and articles of interest to the public in general, and the veteran community in particular. We strive to offer timely, current, and informative content, with the occasional piece focused on entertainment. We are continually expanding and striving to improve the readers’ experience.

© 2024 The Havok Journal

[email protected]

The Havok Journal welcomes re-posting of our original content as long as it is done in compliance with our Terms of Use.

Like this article?

More articles related to this.

Gonadorelin is a synthetic decapeptide designed to mimic the effects of the gonadotropin-releasing hormone. Studies…

If you’re feeling stuck in your current career path and you’re ready to take the…

Writing a research paper can be daunting, especially for those new to the process. However,…

Subscribe to our newsletter

Sign up to receive email updates daily and to hear what's going on with us!

• Full Name *
• Email address *

• Privacy Policy

Home » Research Techniques – Methods, Types and Examples

Research Techniques – Methods, Types and Examples

Table of Contents

Research Techniques

Definition:

Research techniques refer to the various methods, processes, and tools used to collect, analyze, and interpret data for the purpose of answering research questions or testing hypotheses.

Methods of Research Techniques

The methods of research techniques refer to the overall approaches or frameworks that guide a research study, including the theoretical perspective, research design, sampling strategy, data collection and analysis techniques, and ethical considerations. Some common methods of research techniques are:

• Quantitative research: This is a research method that focuses on collecting and analyzing numerical data to establish patterns, relationships, and cause-and-effect relationships. Examples of quantitative research techniques are surveys, experiments, and statistical analysis.
• Qualitative research: This is a research method that focuses on collecting and analyzing non-numerical data, such as text, images, and videos, to gain insights into the subjective experiences and perspectives of the participants. Examples of qualitative research techniques are interviews, focus groups, and content analysis.
• Mixed-methods research: This is a research method that combines quantitative and qualitative research techniques to provide a more comprehensive understanding of a research question. Examples of mixed-methods research techniques are surveys with open-ended questions and case studies with statistical analysis.
• Action research: This is a research method that focuses on solving real-world problems by collaborating with stakeholders and using a cyclical process of planning, action, and reflection. Examples of action research techniques are participatory action research and community-based participatory research.
• Experimental research : This is a research method that involves manipulating one or more variables to observe the effect on an outcome, to establish cause-and-effect relationships. Examples of experimental research techniques are randomized controlled trials and quasi-experimental designs.
• Observational research: This is a research method that involves observing and recording behavior or phenomena in natural settings to gain insights into the subject of study. Examples of observational research techniques are naturalistic observation and structured observation.

Types of Research Techniques

There are several types of research techniques used in various fields. Some of the most common ones are:

• Surveys : This is a quantitative research technique that involves collecting data through questionnaires or interviews to gather information from a large group of people.
• Experiments : This is a scientific research technique that involves manipulating one or more variables to observe the effect on an outcome, to establish cause-and-effect relationships.
• Case studies: This is a qualitative research technique that involves in-depth analysis of a single case, such as an individual, group, or event, to understand the complexities of the case.
• Observational studies : This is a research technique that involves observing and recording behavior or phenomena in natural settings to gain insights into the subject of study.
• Content analysis: This is a research technique used to analyze text or other media content to identify patterns, themes, or meanings.
• Focus groups: This is a research technique that involves gathering a small group of people to discuss a topic or issue and provide feedback on a product or service.
• Meta-analysis: This is a statistical research technique that involves combining data from multiple studies to assess the overall effect of a treatment or intervention.
• Action research: This is a research technique used to solve real-world problems by collaborating with stakeholders and using a cyclical process of planning, action, and reflection.
• Interviews : Interviews are another technique used in research, and they can be conducted in person or over the phone. They are often used to gather in-depth information about an individual’s experiences or opinions. For example, a researcher might conduct interviews with cancer patients to learn more about their experiences with treatment.

Example of Research Techniques

Here’s an example of how research techniques might be used by a student conducting a research project:

Let’s say a high school student is interested in investigating the impact of social media on mental health. They could use a variety of research techniques to gather data and analyze their findings, including:

• Literature review : The student could conduct a literature review to gather existing research studies, articles, and books that discuss the relationship between social media and mental health. This will provide a foundation of knowledge on the topic and help the student identify gaps in the research that they could address.
• Surveys : The student could design and distribute a survey to gather information from a sample of individuals about their social media usage and how it affects their mental health. The survey could include questions about the frequency of social media use, the types of content consumed, and how it makes them feel.
• Interviews : The student could conduct interviews with individuals who have experienced mental health issues and ask them about their social media use, and how it has impacted their mental health. This could provide a more in-depth understanding of how social media affects people on an individual level.
• Data analysis : The student could use statistical software to analyze the data collected from the surveys and interviews. This would allow them to identify patterns and relationships between social media usage and mental health outcomes.
• Report writing : Based on the findings from their research, the student could write a report that summarizes their research methods, findings, and conclusions. They could present their report to their peers or their teacher to share their insights on the topic.

Overall, by using a combination of research techniques, the student can investigate their research question thoroughly and systematically, and make meaningful contributions to the field of social media and mental health research.

Purpose of Research Techniques

The Purposes of Research Techniques are as follows:

• To investigate and gain knowledge about a particular phenomenon or topic
• To generate new ideas and theories
• To test existing theories and hypotheses
• To identify and evaluate potential solutions to problems
• To gather data and evidence to inform decision-making
• To identify trends and patterns in data
• To explore cause-and-effect relationships between variables
• To develop and refine measurement tools and methodologies
• To establish the reliability and validity of research findings
• To communicate research findings to others in a clear and concise manner.

Applications of Research Techniques

Here are some applications of research techniques:

• Scientific research: to explore, investigate and understand natural phenomena, and to generate new knowledge and theories.
• Market research: to collect and analyze data about consumer behavior, preferences, and trends, and to help businesses make informed decisions about product development, pricing, and marketing strategies.
• Medical research : to study diseases and their treatments, and to develop new medicines, therapies, and medical technologies.
• Social research : to explore and understand human behavior, attitudes, and values, and to inform public policy decisions related to education, health care, social welfare, and other areas.
• Educational research : to study teaching and learning processes, and to develop effective teaching methods and instructional materials.
• Environmental research: to investigate the impact of human activities on the environment, and to develop solutions to environmental problems.
• Engineering Research: to design, develop, and improve products, processes, and systems, and to optimize their performance and efficiency.
• Criminal justice research : to study crime patterns, causes, and prevention strategies, and to evaluate the effectiveness of criminal justice policies and programs.
• Psychological research : to investigate human cognition, emotion, and behavior, and to develop interventions to address mental health issues.
• Historical research: to study past events, societies, and cultures, and to develop an understanding of how they shape our present.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Data Collection – Methods Types and Examples

Delimitations in Research – Types, Examples and...

Research Process – Steps, Examples and Tips

Research Design – Types, Methods and Examples

Institutional Review Board – Application Sample...

Evaluating Research – Process, Examples and...

Improving recognition of deteriorated historical Persian geometric patterns by fusion decision methods

• S.I.: Visual Pattern Recognition and Extraction for Cultural Heritage
• Published: 01 June 2024

Cite this article

• Bita Hajebi 1   na1 &
• Pooya Hajebi   ORCID: orcid.org/0000-0002-4498-1707 2 , 3   na1  

Historical architecture has different special styles attributed to each era, dynasty, or region. These styles are common features such as geometric properties, ratios, scales, colors, and artistic techniques. Historical geometric ornaments have an enormous capability for classification based on their geometric characteristics. Smart pattern recognition allows researchers to classify huge databases of heritage for useful internet searches. So, our main goal in this paper is to implement the detection of categories in geometric patterns for classification and documentation in which by the photography of ornaments in every monument, the type of patterns and the number of every type of pattern would be estimated quickly. Furthermore, due to occurring deterioration in these patterns, our method also contributes to recognizing the deteriorated patterns. When we encounter numerous pieces of deteriorated patterns, manual recognition in order to reassemble and reconstruct is usually impossible or time-consuming. With the aid of artificial intelligence, in this paper, our aim is to seek to solve the automatic recognition of historical geometric patterns, even patterns having deterioration as an occlusion via image processing and machine learning methods. A challenging issue that researchers would tackle in detecting historical geometric pattern’s types is the variety in geometric textures, especially when they have occlusion such as deterioration. This issue leads to limited success in classifying via extracting only one feature. The other issue is that the extracted feature must be invariant to the transformation, such as scale, rotation, and noise variation. To cope with the challenges mentioned above and accurately classify, we plan to use the fusion method based on extracting global and local features. So, the features extracted from images in this research are based on local and global. In other words, the proposed fusion strategy lies both in feature and decision level, but the core is the proposed three combination methods in fusion decision methods. In this method, the dataset is composed of four main Persian geometric pattern types: Tond dah, Kond tablghenas, Hashtva 4 lengeh, and HashtvatablKond. So, the model will be trained by extracting global and local features of the images separately. Random forest, as the prevalent machine learning algorithm, is proposed for training data and predicting the class of input images. Finally, the probability of prediction for random forest classifiers is fused by the Decision Templates (DT) combiner, Naïve Bayes (NB) combiner, and Dempster–Shafer combination methods. In comparison with the individual classifier accuracy results of 80% and 85% for global and local features respectively, our proposed approach achieves an improved accuracy of 90%, 88%, and 90% in three fusion decision methods including DT combiner, NB combiner, and Dempster–Shafer combination methods.

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

Data will be made available on reasonable request.

Grünbaum B, Shephard GC (1992) Interlace patterns in Islamic and Moorish art. Leonardo, pp. 331–339

Abas SJ, Salman AS (1994) Symmetries of Islamic geometrical patterns. World Scientific, London

Book   Google Scholar  

Nasri A, Benslimane R (2015) A rotation symmetry group detection technique for the characterization of Islamic Rosette Patterns. Pattern Recogn Lett 68:111–117

Article   Google Scholar  

Djibril MO, Hadi Y, Thami ROH (2006) Fundamental region based indexing and classification of islamic star pattern images. In: International conference image analysis and recognition, pp 865–876. Springer, Berlin, Heidelberg

Ahadian M, Bastanfard A (2011, August) Classification of islamic geometric pattern images using Zernike moments. In: 2011 Eighth International Conference Computer Graphics, Imaging and Visualization, pp 19–24. IEEE

Ahadian M, Bastanfard A (2011) Islamic star pattern images recognition by central moment invariants. In: Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), p 1. The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp)

Aoulalay A, El Mhouti A, Massar M (2022, March) Classification of Islamic geometric patterns based on machine learning techniques. In 2022 2nd international conference on innovative research in applied science, engineering and technology (IRASET), pp 1–6. IEEE

Corradini E, Porcino G, Scopelliti A, Ursino D, Virgili L (2022) Fine-tuning SalGAN and PathGAN for extending saliency map and gaze path prediction from natural images to websites. Expert Syst Appl 191:116282

Amelio A, Bonifazi G, Corradini E, Di Saverio S, Marchetti M, Ursino D, Virgili L (2022) Defining a deep neural network ensemble for identifying fabric colors. Appl Soft Comput 130:109687

Amelio A, Bonifazi G, Corradini E, Ursino D, Virgili L (2023) A multilayer network-based approach to represent, explore and handle convolutional neural networks. Cogn Comput 15(1):61–89

Amelio A, Bonifazi G, Cauteruccio F, Corradini E, Marchetti M, Ursino D, Virgili L (2023) Representation and compression of Residual Neural Networks through a multilayer network based approach. Expert Syst Appl 215:119391

Zaklouta F, Stanciulescu B (2014) Real-time traffic sign recognition in three stages. Robot Auton Syst 62(1):16–24

Singh C, Walia E, Mittal N (2012, March) Fusion of Zernike moments and SIFT features for improved face recognition. In: Proceedings of the international conference on recent advances and future trends in information technology, Punjab, India, pp 21–23

Lowe David G (2004) Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

Bay H, Tuytelaars T, Gool LV (2006) Surf: speeded up robust features. In: European conference on computer vision, pp 404–417. Springer, Berlin

Rublee E, Rabaud V, Konolige K, Bradski G (2011) ORB: an efficient alternative to SIFT or SURF. In: 2011 International conference on computer vision, pp 2564–2571. IEEE

Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

Brodić D, Amelio A, Milivojević ZN (2015) Characterization and distinction between closely related south Slavic languages on the example of Serbian and Croatian. In Computer Analysis of Images and Patterns: 16th International Conference, CAIP 2015, Valletta, Malta, September 2-4, 2015 Proceedings, Part I 16 (pp. 654–666). Springer International Publishing

Hajebi B, Hajebi P (2021) Intelligent restoration of historical parametric geometric patterns by Zernike moments and neural networks. J Comput Cult Heritage (JOCCH) 14(4):1–27

Gultom Y, Arymurthy AM, Masikome RJ (2018) Intelligent restoration of historical parametric geometric patterns by Zernike moments and neural networks. Jurnal Ilmu Komputer dan Informasi 11(2):59–66

Altun S, GÜNEŞ MC (2020) Classification of historic ornaments with CNN: issues for interdisciplinary studies. J Comput Des 1(3):115–130

Flusser J, Suk T, Zitova B (2016) 2D and 3D image analysis by moments. Wiley, London

Kim YS, Kim WY (1998) Content-based trademark retrieval system using a visually salient feature. Image Vis Comput 16(12–13):931–939

Flusser J, Zitova B, Suk T (2009) Moments and moment invariants in pattern recognition. Wiley, London

Bin Y, Jia-Xiong P (2002) Invariance analysis of improved Zernike moments. J Opt A Pure Appl Opt 4(6):606

Nor’aini AJ, Raveendran P, Selvanathan N (2006) Human face recognition using Zernike moments and nearest neighbor classifier. In: 2006 4th student conference on research and development, pp 120–123. IEEE

Padilla-Vivanco A, Martinez-Ramirez A, Granados-Agustin F-S (2004) Digital image reconstruction using Zernike moments. In: Optics in atmospheric propagation and adaptive systems VI, Vol. 5237. International Society for Optics and Photonics, pp 281–289

Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 6:610–621

Singhal N, Singhal N, Kalaichelvi V (2017) Image classification using bag of visual words model with FAST and FREAK. In: 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp 1–5. IEEE

Calonder M, Lepetit V, Strecha C, Brief FP, Binary robust independent elementary features. In: Proceedings of the European Conference on Computer Vision, pp 778–792

Rosten E, Drummond T (2006) Machine learning for high-speed corner detection. In: European conference on computer vision, pp 430–443. Springer, Berlin

Rosin PL (1999) Measuring corner properties. Comput Vis Image Underst 73(2):291–307

Brown M, Szeliski R, Winder S (2005) Multi-image matching using multi-scale oriented patches. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), Vol 1, pp 510–517. IEEE

Ho TK (1995) Random decision forests. In: Proceedings of 3rd international conference on document analysis and recognition, Vol 1, pp 278–282. IEEE

Breiman L (2001) Random forests. Mach Learn 45(1):5–32

Kuncheva LI (2014) Combining pattern classifiers: methods and algorithms. Wiley, London

Yager RR (1987) On the Dempster-Shafer framework and new combination rules. Inf Sci 41(2):93–137

Article   MathSciNet   Google Scholar  

Gilpin LH, Bau D, Yuan BZ, Bajwa A, Specter M, Kagal L (2018) Explaining explanations: an overview of interpretability of machine learning. In: 2018 IEEE 5th International Conference on data science and advanced analytics (DSAA), pp 80–89. IEEE

Doshi-Velez F, Kim B (2017) Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608

Vilone G, Longo L (2021) Notions of explainability and evaluation approaches for explainable artificial intelligence. Inf Fusion 76:89–106

Download references

Acknowledgements

This project was funded by Iran Science Elites Federation. The authors are thankful to Dr. Leily A.Bakhtiar for her support and critical comments regarding this article.

Author information

Bita Hajebi and Pooya Hajebi contributed equally to this work.

Authors and Affiliations

Department of Architectural and Urban Conservation, Art University of Isfahan, Hakim Nezami, Isfahan, 8175894418, Esfahan, Iran

Bita Hajebi

Department of Electrical Engineering, Yazd University, University Boulevard, Yazd, 8915818411, Yazd, Iran

Pooya Hajebi

Department of Mechanical Engineering, Isfahan University of Technology, Isfahan, 8415683111, Iran

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Pooya Hajebi .

Ethics declarations

Conflict of interest.

The authors have no Conflict of interest to declare that are relevant to the content of this article.

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

Hajebi, B., Hajebi, P. Improving recognition of deteriorated historical Persian geometric patterns by fusion decision methods. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-09932-3

Download citation

Received : 21 April 2023

Accepted : 28 April 2024

Published : 01 June 2024

DOI : https://doi.org/10.1007/s00521-024-09932-3

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

• Geometric Patterns
• Fusion decision Methods
• Dempster–Shafer combination Method
• Decision Templates (DT)
• Naïve Bayes (NB) combiner

Advertisement

• 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
• Turk J Urol
• v.39(Suppl 1); 2013 Sep

How to write a materials and methods section of a scientific article?

In contrast to past centuries, scientific researchers have been currently conducted systematically in all countries as part of an education strategy. As a consequence, scientists have published thousands of reports. Writing an effective article is generally a significant problem for researchers. All parts of an article, specifically the abstract, material and methods, results, discussion and references sections should contain certain features that should always be considered before sending a manuscript to a journal for publication. It is generally known that the material and methods section is a relatively easy section of an article to write. Therefore, it is often a good idea to begin by writing the materials and methods section, which is also a crucial part of an article. Because “reproducible results” are very important in science, a detailed account of the study should be given in this section. If the authors provide sufficient detail, other scientists can repeat their experiments to verify their findings. It is generally recommended that the materials and methods should be written in the past tense, either in active or passive voice. In this section, ethical approval, study dates, number of subjects, groups, evaluation criteria, exclusion criteria and statistical methods should be described sequentially. It should be noted that a well-written materials and methods section markedly enhances the chances of an article being published.

How to Write a Materials and Methods Section of a Scientific Article?

Up to the 18 th Century scientific researches were performed on a voluntary basis by certain scientists. However from the second half of the 19 th century, scientific development has gained momentum with the contributions of numerous scientists including Edison, Fleming, and Koch. In parallel with these developments, apparently each scientific field, and even their branches made, and still making magnificent progressions from the end of the 18 th century. Secondary to these developments, scientific researches have been implemented systematically by universities, and various institutions in every part of the world as an integral component of national strategies. Naturally, the number of researchers who performed scientific investigations or sponsored by various institutions increased considerably. Also, as is known very well, all over the world scientists, and researchers move from one place to another to disseminate scientific knowledge. All of these scientific efforts, and activities reflect on clinical practice, and hundreds of thousands, and millions scientific articles which we can currently gain access into all of them online. As indicated by the investigator Gerard Piel, “Without publication, science is dead” which explains the importance of publication. In other words, if you don’t share your investigation and knowledge, they don’t mean anything by themselves. Although sharing the knowledge is essential for writing a scientific paper, nowadays writing a scientific article is mostly learnt as a master-apprentice relationship, and therefore certain standards have not been established. This phenomenon creates serious stress especially for young investigators in their early stage of writing scientific papers. Indeed investigators receiving their residency training confront this reality finally during writing of their dissertations. Though sharing knowledge is known as a fundamental principle in writing a scientific paper, it creates difficulties in the whole world. Relevant to this issue, in the whole world investigations have been performed, and books have been written on the subject of how to write a scientific paper. Accordingly, in our country mostly local meetings, and courses have been organized. These organizations, and investigations should be performed. Indeed, nowadays, in the first assessments, the rejection rate of the journals by internationally acknowledged scientific indexes as “Science Citation İndex (SCI)” and “Science Citation İndex Expanded (SCI-extended” which have certain scientific standards, increases to 62 percent. As a matter of fact only 25% of Class A journals have been included in the lists of SCI, and SCI-extended.

As we all know very well, scientific articles consist of sections of summary, introduction, material, and methods, discussion, and references. Among them, conventionally Materials and Methods section has been reported as the most easily written or will be written section. Although it is known as the most easily written section, nearly 30% of the reasons for rejection are related to this section per se. Therefore due care, and attention should be given to the writing of this section. In the writing process of the ‘Material and Methods’ section, all achievements performed throughout the study period should be dealt with in consideration of certain criteria in a specific sequence. Since as a globally anticipated viewpoint, ‘Materials and Methods’ section can be written quite easily, it has been indicated that if difficulties are encountered in writing a manuscript, then one should start writing from this section. In writing this section, study design describing the type of the article, study subjects to be investigated, methods, and procedures of measurements should be provided under four main headings. [ 1 , 2 ] Accordingly, in brief, we can emphasize the importance of providing clear-cut, adequate, and detailed information in the ‘Materials and Methods’ section to the scientists who will read this scientific article. Meeting these criteria carries great importance with respect to the evaluation of reliability of the investigation by the readers, and reviewers, and also informing them about procedural method, design, data collection, and assessment methods of the investigation, Priorly, as is the case in all scientific investigations, one should be reminded about the importance, and indispensability of compliance with certain standard writing rules. Accordingly, rules of grammar should be obeyed, and if possible passive voice of simple past tense should be used. Related to these rules, use of verbs ‘investigated’, ‘evaluated’ or ‘performed’ will be appropriate. Recently, expressions showing the ownership of the investigation as ‘we performed’, ‘we evaluated’, ‘we implemented’ have taken priority. Since the important point is communication of the message contained in the scientific study, the message should be clearly comprehensible. While ensuring clarity of the message, use of flourishing, and irrelevant sentences should be avoided. [ 1 , 3 ] According to another approach, since our article will be read by professionals of other disciplines, it is important to comply with certain rules of writing. To that end, standard units of measurements, and international abbreviations should be used. Abbreviations should be explained within parentheses at their first mention in the manuscript. For instance let’s analyze the following sentence” The patients were evaluated with detailed medical history, physical examination, complete urinalysis, PSA, and urinary system ultrasound” The abbreviation PSA is very well known by the urologist. However we shouldn’t forget that this article will be read by the professionals in other medical disciplines. Similarly this sentence should not be written as: “The patients were evaluated with detailed medical history, physical examination, complete urinalysis PSA (prostate-specific antigen), and urinary system ultrasound.” Indeed the abbreviation should follow the explanation of this abbreviation. Then the appropriate expression of the sentence should be. “The patients were evaluated with detailed medical history, physical examination, complete urinalysis, prostate-specific antigen (PSA), and urinary system ultrasound.”

In addition to the abovementioned information, in the beginning paragraphs of ‘Materials and Methods’ section of a clinical study the answers to the following questions should be absolutely provided:

• The beginning, and termination dates of the study period.
• Number of subjects/patients/experimental animals etc. enrolled in the study,
• Has the approval of the ethics committee been obtained?
• Study design (prospective, retrospective or other). [ 1 , 2 , 4 – 7 ]

Still additional features of the study design (cross-sectional) should be indicated. Apart from this, other types of study designs (randomized, double-blind, placebo-controlled or double-blind, parallel control etc.) should be revealed.

The heading of the section “Materials and Methods” can be changed to “Patients and the Method” in accordance with writing rules of the journal in question. Indication of starting, and termination dates of a clinical study will facilitate scientific interpretation of the article. Accordingly, outcomes obtained during development phase of a newly implemented method might be considered differently from those acquired during conventional use of this method. Besides, incidence of the diseases, and number of affected people might vary under the impact of social fluctuations, and environmental factors. Therefore with this justification study period should be specified. Number of cases included in the study should be absolutely indicated in the ‘Materials and Methods’ section. It will be appropriate to determine study population after consultation to a statistician-and if required-following “power analysis” Accordingly, the need for a control group will be indicated based on the study design. Nowadays, as a requirement of patient rights, obtainment of approval from ethics committee should be indicated with its registration number. In addition, acquirement of informed consent forms from patients should be indicated. Ethics Committee approval should be obtained in prospective studies performed with study drugs. Otherwise in case of occurrence of adverse effects, it should be acknowledged that in compliance with Article #90 of the Turkish Criminal Law, a 3-year prison sentence is given to the guilty parties. [ 8 ] Since issues related to the Ethics Committee are the subject of another manuscript, they won’t be handled herein.

The following paragraph exemplifies clearly the aforementioned arguments: “After approval of the local ethics committee (BADK-22), informed consent forms from the patients were obtained, and a total of 176 cases with lower urinary tract symptoms (LUTS) were retrospectively evaluated between January 2011, and December 2012.” In a prospectively designed study, methods used to communicate with the cases including face-to-face interviews, phone calls and/or e-mail should be indicated. [ 1 , 2 ] Each paragraph or subheading in the ‘Materials and Methods’ section should be in accordance with the related ones in the ‘Results’ section. In other words, the sequence of paragraphs, and subheadings in the ‘Results’ section should be the same in the ‘Materials and Methods’ section.

As a next step, names of the groups, and distribution of the cases in these groups should be indicated. For instance: the statement “Cases were divided into 3 groups based on their LUTS scores as. Groups 1 (0–9; n=91), 2 (10–18; n=66), and 3 (≥19; n=20)” clearly delineates the scope of the study at baseline.. In the ‘Materials and Methods’ section the number of study subjects should be absolutely documented. Herein, after assignment of names to groups, in the rest of the manuscript, these names should be used. For example instead of saying: “Mean ages of the cases with LUTS scores between 0–9, 10–18, and ≥19 were determined to be 63.2±2.1, 62.8±4.5, and 65.7±3.9 years, respectively” it will be more comprehensible to use the expression: “Mean ages of the Groups 1, 2, and 3 were specified as 63.2±2.1, 62.8±4.5, and 65.7±3.9 years.” (p=0.478). Expressions indicated in the ‘Materials and Methods’ section should not be repeated in the “Results” section. Thus, errors of repetition will be precluded. Following the abovementioned information, the evaluation method of the cases enrolled in the study should be indicated. Hence, results of medical history, physical examination, and if performed laboratory or radiological evaluations-in that order-should be indicated. The application of survey study-if any-should be investigated, and documented. Therefore, the following sentences encompass all the information stated above: “The cases were evaluated with detailed medical history, physical examination, measurements of serum follicle stimulating hormone (FSH), luteinizing hormone (LH), testosterone (T) levels, complete urinalysis, urinary flow rate, direct urinary system roentgenograms, urinary system ultrasound, and if required cyctoscopy. Lower urinary system complaints, and erectile dysfunction were evaluated using International Prostate Symptom Score (IPSS), and International Erectile Function Scale (IIEF), respectively.” Apparently, questionnaire forms were used in the above-cited study. However, methods used for the evaluation of questionnaire forms, and significance of the results obtained, and if possible, the first performer of this survey should be written with accompanying references. In relation to the abovementioned questionnaires the following statements constitute standard expressions for the ‘Materials and Methods’ section: “International Prostate Symptom Score (IPPS) was used in the determination of the severity of prostatic symptoms. IPSS used to determine the severity of the disease, evaluate treatment response, and ascertain the symptomatic progression, is the most optimal scoring system recommended by European Association of Urology (EAU) which classifies the severity of the disease based on IPSS scores as mild (0–7), moderate (8–19), and severe symptomatic (20–35) disease. In the evaluation of sexual function International Erectile Function Scale (IIEF) was used. IIEF is one of the most prevalently used form for the patients who consulted for the complaints of sexual dysfunction Based on IIEF scores, the severity of the disease was classified as severe (1–10), moderate (11–16), mild to moderate (17–21), mild (22–25), and no ED (26–30).”

Whether the institutions of the authors working for should be written in the ‘Materials and Methods’ section can be a subject of debate, generally viewpoints favour provision of this information. However, in compliance with their writing rules, some journals do not favour open-label studies where name of the study site is indicated, and this principle is communicated to the author during editorial evaluation Besides, in the ‘Materials and Methods’ section, the brand of the study object, and its country of origin should be indicated. (ie. if radiological methods are used, then the brand of radiological equipment, and its manufacturing country should be specified. In a study entitled ‘The Impact of Computed Tomography in the Prediction of Post-Radical Nephrectomy Stage in Renal Tumours’ since the main topic of the study is computed tomography, the specifications of the equipment used should be explicitely indicated. On the other hand, the details of the medical method which can effect the outcomes of the study should be also recorded. Accordingly, the methods applied for percutaneous nephrolithotomy, ureterorenoscopy, varicocelectomy, transurethral prostatectomy, radical prostatectomy (perineal, open, laparoscopic or robotic should be absolutely indicated. Then inclusion, and exclusion criteria, and if used control group, and its characteristics should be documented. Thus the following paragraph about exclusion criteria will be appropriate: Patients with a history of neurogenic bladder, prostatic or abdominal operation, and transrectal ultrasound guided prostate biopsy (within the previous 6 months), those aged <40 or >70 years, individuals with a peak urine flow rate below 10 ml/sec, and residual urine more than 150 cc were not included in the study.” [ 1 – 3 , 9 ]

Some diseases mentioned in the “Materials and Methods” section require special monitorization procedures. In these cases the procedure of monitorization should be documented for the sake of the validity of the study in question. Accordingly, in conditions such as “nephrectomy, prostatectomy, orchidectomy, pyeloplasty, varicocelectomy, drug therapies, penile prosthesis, and urethral stricture” clinical follow-up protocols should be provided.

The abovementioned rules, and recommendations are most frequently valid for a clinical study, and some points indicated in experimental studies should be also considered. Types, weights, gender, and number of the animals used in animal studies should be absolutely specified. Besides condition of evaluation of experimental animals should be noted. Then as is the case with clinical studies, approval of the ethics committee should be obtained, and documented. Accordingly, the beginning paragraphs of the ‘Materials and Methods’ can be expressed as follows:

“In the study, 40 Wistar-Albino 6-month-old rats each weighing 350–400 g were used. After approval of the ethics committee (HADYEK-41) the study was performed within the frame of rules specified by the National Institute for animal experiments. The rats were divided into 3 groups. Hence, Group 1 (n=7) was accepted as the control group. The rats subjected to partial ureteral obstruction with or without oral carvedilol therapy at daily doses of 2 mg/kg maintained for 7 days constituted Groups 3 (n=8), and 2 (n=8), respectively. Each group of 4 rats was housed in standard cages with an area of 40×60 cm. The animals were fed with standard 8 mm food pellets, and fresh daily tap water. The rats were kept in the cages under 12 hours of light, and 12 hours of dark. Ambient temperature, and humidity were set at 22±2°C, and 50±10%, respectively.”

Herein, the method, and agent of anesthesia used (local or general anesthesia) in surgical procedures, and then the experimental method applied should be clearly indicated. For example the following sentences explain our abovementioned arguments; “All surgical procedures were performed under xylazine-ketamine anesthesia. In all groups, ureters were approached through midline abdominal incision. In Group 1, ureters were manipulated without causing obstruction. Results of biochemical, and pathological evaluations performed in Group 1 were considered as baseline values.”

“Through a midline abdominal incision partial ureteral obstruction was achieved by embedding two-thirds of the distal part of the left ureter into psoas muscle using 4/0 silk sutures as described formerly by Wen et al. [ 10 ] ( Figure 1 ). [ 11 ] All rats were subjected to left nephrectomies at the end of the experimental study.” As formulated by the above paragraph, if the method used is not widely utilized, then the first researcher who describes the method should be indicated with relevant references. One or more than one figures with a good resolution, and easily comprehensible legends should be also included in the explanation of the experimental model. For very prevalently used experimental models as torsion models cited in the “Materials and Methods” section, there is no need to include figures in the manuscript.

Partial ureteral obstruction model [ 11 ]

Appropriate signs, and marks placed on the figure will facilitate comprehension of the legends ( Figure 2 ).

Ureteral segments (black arrows) seen in a rat partial ureteral obstruction model [ 11 ]

The signs used will also improve intelligibility of the target. The figures should be indicated within parentheses in their first mention in the “Materials and Methods” section. Headings and as a prevalent convention legends of the figures should be indicated at the end of the manuscript.

If a different method is used in the study, this should be explained in detail. For instance, in a study where the effect of smoking on testes was investigated, the method, and the applicator used to expose rats to cigarette smoke should be indicated in the ‘Methods’ section following classical description. Relevant to the study in question, the following paragraph explaining the study method should be written: “A glass chamber with dimensions of 75 × 50 × 50 cm was prepared, and divided into 4 compartments with wire fences. The rats in the 2., and 4. cages were placed in these compartments. Each compartment contained 4 rats. Cigarette smoke was produced using one cigarette per hour, and smoke coming from the tip, and the filter of the lighted cigarette was pumped into the gas chamber with a pneumatic motor. The rats were exposed to smoke of 6 cigarettes for 6 hours. The compartments of the rats were changed every day so as to achieve balanced exposure of the rats to cigarette smoke.” [ 12 ]

Meanwhile, chemical names, doses, and routes of administration of the substances used in experimental studies should be indicated. If the substance used is a solution or an antibody, then manufacturing firm, and its country should be indicated in parenthesis. This approach can be exemplified as “Animals used in experiments were randomized into 4 groups of 8 animals. Each group was housed in 2 cages each containing 4 animals. The first group did not undergo any additional procedure (Group 1). The second group was exposed to cigarette smoke (Group 2). The third (Group 3), and the fourth (Group 4) groups received daily intraperitoneal injectable doses of 10 mg/kg resveratrol (Sigma-Aldrich, St. Louis, MO, USA). The Group 4 was also exposed to cigarette smoke. [ 12 ]

After all of these procedures, method, and analytical procedure of histopathological examination used should be described-if possible-by a pathologist Similarly, biochemical method used should be referenced, and written by the department of clinical chemistry. It can be inferred that each division should describe its own method. In other words, histopathological, microbiological, and pharmacological method should be described in detail by respective divisions.

If we summarize all the information stated above, understandably sharing of the scientific knowledge is essential.. Since reproducibility of a study demonstrates the robustness of a study, with the detailed approaches indicated above, reproducibility of our study is provided, and the relevant questions of “How?”, and “How much?” are answered. Besides, since ‘Materials, and Methods’, and ‘Results’ sections will constitute a meaningful whole, explanations of all information related to the data mentioned in the ‘Results’ section should be provided. As an important point not to be forgotten, evaluation or measurement method used for each parameter indicated in the ‘Results’ section should be expounded in the “Materials and Methods” section. For example if you used an expression in the” Results” section like “median body mass index (BMI) of the patients was 27.42 kg/m 2 ”, then you should beforehand indicate that comparative evaluation of BMIs will be done in the “Materials and Methods” section. In addition, the description, and significance of the values expressed in the “Results” section should be indicated in the “Materials and Methods” section. In other words, it should be stated that the patients were evaluated based on their BMIs as normal (18–24.9 kg/m 2 ), overweight (25 kg/m 2 –40 kg/m 2 ), and morbid obesity (>40 kg/m 2 ). If you encounter difficulties in writing “Materials and Methods” section, also a valid approach for other sections, firstly simple headings can be written, then you can go into details. In brief, for every parameter, the reader should get clear-cut answers to the questions such as “How did they evaluate this parameter, and which criteria were used?”. [ 1 , 3 , 13 – 15 ]

The last paragraph of the ‘Materials, and Methods’ section should naturally involve statistical evaluations. This section should be written by statisticians. Accordingly, the preferred statistical method, and the justifications for this preference should be indicated. In conventional statistical evaluations, provision of details is not required. In information indicated above, the statement “For statistical analysis, ANOVA test, chi-square test, T test, Kruskal-Wallis test have been used.” is not required very much. Instead, more appropriate expression will be a statement indicating that recommendations of a knowledgeable, and an experienced statistician were taken into consideration or advanced statistical information was reflected on the statistical evaluations as follows: “Chi-square tests were used in intergroup comparisons of categorical variables, and categorical variables were expressed as numbers, and percentages. In comparisons between LUTS, and ED as for age, independent two samples t-test was used. In the evaluation of the factors effective on erectile dysfunction multivariate logistic regresssion test was used. P values lower than 0.05 were considered as statistically significant The calculations were performed using a statistical package program (PASW v18, SPSS Inc, Chicago, IL).” Herein, the type of statistical package used for statistical methods should be emphasized.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 27 May 2024

Research on domain ontology construction based on the content features of online rumors

• Jianbo Zhao 1 ,
• Huailiang Liu 1 ,
• Weili Zhang 1 ,
• Tong Sun 1 ,
• Qiuyi Chen 1 ,
• Yuehai Wang 2 ,
• Jiale Cheng 2 ,
• Yan Zhuang 1 ,
• Xiaojin Zhang 1 ,
• Shanzhuang Zhang 1 ,
• Bowei Li 3 &
• Ruiyu Ding 2  

Scientific Reports volume  14 , Article number:  12134 ( 2024 ) Cite this article

196 Accesses

1 Altmetric

Metrics details

• Computational neuroscience
• Computer science
• Data acquisition
• Data integration
• Data mining
• Data processing
• Human behaviour
• Information technology
• Literature mining
• Machine learning
• Scientific data

Online rumors are widespread and difficult to identify, which bring serious harm to society and individuals. To effectively detect and govern online rumors, it is necessary to conduct in-depth semantic analysis and understand the content features of rumors. This paper proposes a TFI domain ontology construction method, which aims to achieve semantic parsing and reasoning of the rumor text content. This paper starts from the term layer, the frame layer, and the instance layer, and based on the reuse of the top-level ontology, the extraction of core literature content features, and the discovery of new concepts in the real corpus, obtains the core classes (five parent classes and 88 subclasses) of the rumor domain ontology and defines their concept hierarchy. Object properties and data properties are designed to describe relationships between entities or their features, and the instance layer is created according to the real rumor datasets. OWL language is used to encode the ontology, Protégé is used to visualize it, and SWRL rules and pellet reasoner are used to mine and verify implicit knowledge of the ontology, and judge the category of rumor text. This paper constructs a rumor domain ontology with high consistency and reliability.

Similar content being viewed by others

Testing theory of mind in large language models and humans

Investigating child sexual abuse material availability, searches, and users on the anonymous Tor network for a public health intervention strategy

Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors

Introduction.

Online rumors are false information spread through online media, which have the characteristics of wide content 1 , hard to identify 2 , 3 . Online rumors can mislead the public, disrupt social order, damage personal and collective reputations, and pose a great challenge to the governance of internet information content. Therefore, in order to effectively detect and govern online rumors, it is necessary to conduct an in-depth semantic analysis and understanding of the rumor text content features.

The research on the content features of online rumors focuses on the lexical, syntactic and semantic features of the rumor text, including lexical, syntactic and semantic features 4 , syntactic structure and functional features 5 , source features 5 , 6 , rhetorical methods 7 , narrative structure 6 , 7 , 8 , language style 6 , 9 , 10 , corroborative means 10 , 11 and emotional features 10 , 12 , 13 , 14 , 15 , 16 , 17 , 18 . Most of the existing researches on rumor content features are feature mining under a single domain topic type, and lack of mining the influence relationship between multiple features. Therefore, this paper proposes to build an online rumor domain ontology to realize fine-grained hierarchical modeling of the relationship between rumor content features and credible verification of its effectiveness. Domain ontology is a systematic description of the objective existence in a specific discipline 19 . The construction methods mainly include TOVE method 20 , skeleton method 21 , IDEF-5 method 22 , 23 , methontology method 24 , 25 and seven-step method 26 , 27 , among which seven-step method is the most mature and widely used method at present 28 , which has strong systematicness and applicability 29 , but it does not provide quantitative indicators and methods about the quality and effect of ontology. The construction technology can be divided into the construction technology based on thesaurus conversion, the construction technology based on existing ontology reuse and the semi-automatic and automatic construction technology based on ontology engineering method 30 . The construction technology based on thesaurus conversion and the construction technology based on existing ontology reuse can save construction time and cost, and improve ontology reusability and interoperability, but there are often differences in structure, semantics and scene. Semi-automatic and automatic construction technology based on ontology engineering method The application of artificial intelligence technology can automatically extract ontology elements and structures from data sources with high efficiency and low cost, but the quality and accuracy are difficult to guarantee. Traditional domain ontology construction methods lack effective quality evaluation support, and construction technology lacks effective integration application. Therefore, this paper proposes an improved TFI network rumor domain ontology construction method based on the seven-step method. Starting from the terminology layer, the framework layer and the instance layer, it integrates the top-level ontology and core document content feature reuse technology, the bottom-up semi-automatic construction technology based on N-gram new word discovery algorithm and RoBERTa-Kmeans clustering algorithm, defines the fine-grained features of network rumor content and carries out hierarchical modeling. Using SWRL rules and pellet inference machine, the tacit knowledge of ontology is mined, and the quality of ontology validity and consistency is evaluated and verified.

The structure of this paper is as follows: Sect “ Related work ” introduces the characteristics of rumor content and the related work of domain ontology construction.; Sect “ Research method ” constructs the term layer, the frame layer and the instance layer of the domain ontology; Sect “ Domain ontology construction ” mines and verifies the implicit knowledge of the ontology based on SWRL rules and Pellet reasoner; Sect “ Ontology reasoning and validation ” points out the research limitations and future research directions; Sect “ Discussion ” summarizes the research content and contribution; Sect “ Conclusion ” summarizes the research content and contribution of this paper.

Related Work

Content features of online rumors.

The content features of online rumors refer to the adaptive description of vocabulary, syntax and semantics in rumor texts. Fu et al. 5 have made a linguistic analysis of COVID-19’s online rumors from the perspectives of pragmatics, discourse analysis and syntax, and concluded that the source of information, the specific place and time of the event, the length of the title and statement, and the emotions aroused are the important characteristics to judge the authenticity of the rumors; Zhang et al. 6 summarized the narrative theme, narrative characteristics, topic characteristics, language style and source characteristics of new media rumors; Li et al. 7 found that rumors have authoritative blessing and fear appeal in headline rhetoric, and they use news and digital headlines extensively, and the topic construction mostly uses programmed fixed structure; Yu et al. 8 analyzed and summarized the content distribution, narrative structure, topic scene construction and title characteristics of rumors in detail; Mourao et al. 9 found that the language style of rumors is significantly different from that of real texts, and rumors tend to use simpler, more emotional and more radical discourse strategies; Zhou et al. 10 analyzed the rumor text based on six analysis categories, such as content type, focus object and corroboration means, and found that the epidemic rumors were mostly “infectious” topics, with narrative expression being the most common, strong fear, and preference for exaggerated and polarized discourse style. Huang et al. 11 conducted an empirical study based on WeChat rumors, and found that the “confirmation” means of rumors include data corroboration and specific information, hot events and authoritative release; Butt et al. 12 analyzed the psycholinguistic features of rumors, and extracted four features from the rumor data set: LIWC, readability, senticnet and emotions. Zhou et al. 13 analyzed the semantic features of fake news content in theme and emotion, and found that the distribution of fake news and real news is different in theme features, and the overall mood, negative mood and anger of fake news are higher; Tan et al. 14 divided the content characteristics of rumors into content characteristics with certain emotional tendency and social characteristics that affect credibility; Damstra et al. 15 identified the elements as a consistent indicator of intentionally deceptive news content, including negative emotions causing anger or fear, lengthy sensational headlines, using informal language or swearing, etc. Lai et al. 16 put forward that emotional rumors can make the rumor audience have similar positive and negative emotions through emotional contagion; Yuan et al. 17 found that multimedia evidence form and topic shaping are important means to create rumors, which mostly convey negative emotions of fear and anger, and the provision of information sources is related to the popularity and duration of rumors; Ruan et al. 18 analyzed the content types, emotional types and discourse focus of Weibo’s rumor samples, and found that the proportion of social life rumors was the highest, and the emotional types were mainly hostile and fearful, with the focus on the general public and the personnel of the party, government and military institutions.

The forms and contents of online rumors tend to be diversified and complicated. The existing research on the content features of rumors is mostly aimed at the mining of content characteristics under specific topics, which cannot cover various types of rumor topics, and lacks fine-grained hierarchical modeling of the relationship between features and credible verification of their effectiveness.

Domain ontology construction

Domain ontology is a unified definition, standardized organization and visual representation of the concepts of knowledge in a specific domain 31 , 32 , and it is an important source of information for knowledge-based systems 19 , 33 . Theoretical methods include TOVE method 20 , skeleton method 21 , IDEF-5 method 22 , 23 , methontology method 24 , 25 and seven-step method 26 , 27 . TOVE method transforms informal description into formal ontology, which is suitable for fields that need accurate knowledge, but it is complex and time-consuming, requires high-level domain knowledge and is not easy to expand and maintain. Skeleton method forms an ontology skeleton by defining the concepts and relationships of goals, activities, resources, organizations and environment, which can be adjusted according to needs and is suitable for fields that need multi-perspective and multi-level knowledge, but it lacks formal semantics and reasoning ability. Based on this method, Ran et al. 34 constructed the ontology of idioms and allusions. IDEF5 method uses chart language and detailed description language to construct ontology, formalizes and visualizes objective knowledge, and is suitable for fields that need multi-source data and multi-participation, but it lacks a unified ontology representation language. Based on this method, Li et al. 35 constructed the business process activity ontology of military equipment maintenance support, and Song et al. 36 established the air defense and anti-missile operation process ontology. Methontology is a method close to software engineering. It systematically develops ontologies through the processes of specification, knowledge acquisition, conceptualization, integration, implementation, evaluation and document arrangement, which is suitable for fields that need multi-technology and multi-ontology integration, but it is too complicated and tedious, and requires a lot of resources and time 37 . Based on this method, Yang et al. 38 completed the ontology of emergency plan, Duan et al. 39 established the ontology of high-resolution images of rural residents, and Chen et al. 40 constructed the corpus ontology of Jiangui. Seven-step method is the most mature and widely used method at present 28 . It is systematic and applicable to construct ontology by determining its purpose, scope, terms, structure, attributes, limitations and examples 29 , but it does not provide quantitative indicators and methods about the quality and effect of ontology. Based on this method, Zhu et al. 41 constructed the disease ontology of asthma, Li et al. 42 constructed the ontology of military events, the ontology of weapons and equipment and the ontology model of battlefield environment, and Zhang et al. 43 constructed the ontology of stroke nursing field, and verified the construction results by expert consultation.

Domain ontology construction technology includes thesaurus conversion, existing ontology reuse and semi-automatic and automatic construction technology based on ontology engineering method 30 . The construction technology based on thesaurus transformation takes the existing thesaurus as the knowledge source, and transforms the concepts, terms and relationships in the thesaurus into the entities and relationships of domain ontology through certain rules and methods, which saves the time and cost of ontology construction and improves the quality and reusability of ontology. However, it is necessary to solve the structural and semantic differences between thesaurus and ontology and adjust and optimize them according to the characteristics of different fields and application scenarios. Wu et al. 44 constructed the ontology of the natural gas market according to the thesaurus of the natural gas market and the mapping of subject words to ontology, and Li et al. 45 constructed the ontology of the medical field according to the Chinese medical thesaurus. The construction technology based on existing ontology reuse uses existing ontologies or knowledge resources to generate new domain ontologies through modification, expansion, merger and mapping, which saves time and cost and improves the consistency and interoperability of ontologies, but it also needs to solve semantic differences and conflicts between ontologies. Chen et al. 46 reuse the top-level framework of scientific evidence source information ontology (SEPIO) and traditional Chinese medicine language system (TCMLS) to construct the ontology of clinical trials of traditional Chinese medicine, and Xiao et al. 47 construct the domain ontology of COVID-19 by extracting the existing ontology and the knowledge related to COVID-19 in the diagnosis and treatment guide. Semi-automatic and automatic construction technology based on ontology engineering method semi-automatically or automatically extracts the elements and structures of ontology from data sources by using natural language processing, machine learning and other technologies to realize large-scale, fast and low-cost domain ontology construction 48 , but there are technical difficulties, the quality and accuracy of knowledge extraction can not be well guaranteed, and the quality and consistency of different knowledge sources need to be considered. Suet al. 48 used regular templates and clustering algorithm to construct the ontology of port machinery, Zheng et al. 49 realized the automatic construction of mobile phone ontology through LDA and other models, Dong et al. 50 realized the automatic construction of ontology for human–machine ternary data fusion in manufacturing field, Linli et al. 51 proposed an ontology learning algorithm based on hypergraph, and Zhai et al. 52 learned from it through part-of-speech tagging, dependency syntax analysis and pattern matching.

At present, domain ontology construction methods are not easy to expand, lack of effective quality evaluation support, lack of effective integration and application of construction technology, construction divorced from reality can not guide subsequent practice, subjective ontology verification and so on. Aiming at the problems existing in the research of content characteristics and domain ontology construction of online rumors, this paper proposes an improved TFI network rumor domain ontology construction method based on seven-step method, which combines top-down existing ontology reuse technology with bottom-up semi-automatic construction technology, and establishes rumor domain ontology based on top-level ontology reuse, core document content feature extraction and new concept discovery in the real corpus from the terminology layer, framework layer and instance layer. Using Protégé as a visualization tool, the implicit knowledge mining of ontology is carried out by constructing SWRL rules to verify the semantic parsing ability and consistency of domain ontology.

Research method

This paper proposes a TFI online rumor domain ontology construction method based on the improvement of the seven-step method, which includes the term layer, the frame layer and the instance layer construction.

Term layer construction

Determine the domain and scope: the purpose of constructing the rumor domain ontology is to support the credible detection and governance of online rumors, and the domain and scope of the ontology are determined by answering questions.

Three-dimensional term set construction: investigate the top-level ontology and related core literature, complete the mapping of reusable top-level ontology and rumor content feature concept extraction semi-automatically from top to bottom; establish authoritative real rumor datasets, and complete the domain new concept discovery automatically from bottom to top; based on this, determine the term set of the domain ontology.

Frame layer construction

Define core classes and hierarchical relationships: combine the concepts of the three-dimensional rumor term set, based on the data distribution of the rumor dataset, define the parent class, summarize the subclasses, design hierarchical relationships and explain the content of each class.

Define core properties and facets of properties: in order to achieve deep semantic parsing of rumor text contents, define object properties, data properties and property facets for each category in the ontology.

Instance layer construction

Create instances: analyze the real rumor dataset, extract instance data, and add them to the corresponding concepts in the ontology.

Encode and visualize ontology: use OWL language to encode ontology, and use Protégé to visualize ontology, so that ontology can be understood and operated by computer.

Ontology verification: use SWRL rules and pellet reasoner to mine implicit knowledge of ontology, and verify its semantic parsing ability and consistency.

Ethical statements

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

Determine the professional domain and scope of the ontology description

This paper determines the domain and scope of the online rumor domain ontology by answering the following four questions:

(1) What is the domain covered by the ontology?

The “Rumor Domain Ontology” constructed in this paper only considers content features, not user features and propagation features; the data covers six rumor types of politics and military, disease prevention and treatment, social life, science and technology, nutrition and health, and others involved in China’s mainstream internet rumor-refuting websites.

(2) What is the purpose of the ontology?

To perform fine-grained hierarchical modeling of the relationships among the features of multi-domain online rumor contents, realize semantic parsing and credibility reasoning verification of rumor texts, and guide fine-grained rumor detection and governance. It can also be used as a guiding framework and constraint condition for online rumor knowledge graph construction.

(3) What kind of questions should the information in the ontology provide answers for?

To provide answers for questions such as the fine-grained rumor types of rumor instances, the valid features of rumor types, etc.

(4) Who will use the ontology in the future?

Users of online rumor detection and governance, users of online rumor knowledge graphs construction.

Three-dimensional term set construction

Domain concepts reused by top-level ontology.

As a mature and authoritative common ontology, top-level ontology can be shared and reused in a large range, providing reference and support for the construction of domain ontology. The domain ontology of online rumors established in this paper focuses on the content characteristics, mainly including the content theme, events and emotions of rumor texts. By reusing the terminology concepts in the existing top-level ontology, the terminology in the terminology set can be unified and standardized. At the same time, the top-level concept and its subclass structure can guide the framework construction of domain ontology and reduce the difficulty and cost of ontology construction. Reusable top-level ontologies include: SUMO, senticnet and ERE after screening.

SUMO ontology: a public upper-level knowledge ontology containing some general concepts and relations for describing knowledge in different domains. The partial reusable SUMO top-level concepts and subclasses selected in this paper are shown in Table 1 , which provides support for the sub-concept design of text topics in rumor domain ontology.

Senticnet: a knowledge base for concept-based sentiment analysis, which contains semantic, emotional, and polarity information related to natural language concepts. The partial reusable SenticNet top-level concepts and subclasses selected in this paper are shown in Table 2 , which provides support for the sub-concept design of text topics in rumor domain ontology.

Entities, relations, and events (ERE): a knowledge base of events and entity relations. The partial reusable ERE top-level concepts and subclasses selected in this paper are shown in Table 3 , which provides support for the sub-concept design of text elements in the rumor domain ontology.

Extracting domain concepts based on core literature content features

Domain core literature is an important source for extracting feature concepts. This paper uses ‘rumor detection’ as the search term to retrieve 274 WOS papers and 257 CNKI papers from the WOS and CNKI core literature databases. The content features of rumor texts involved in the literature samples are extracted, the repetition content features are eliminated, the core content features are screened, and the canonical naming of synonymous concepts from different literatures yields the domain concepts as shown in Table 4 . Among them, text theme, text element, text style, text feature and text rhetoric are classified as text features; emotional category, emotional appeal and rumor motive are classified as emotional characteristics; source credibility, evidence credibility and testimony method are classified as information credibility characteristics; social context is implicit.

Extracting domain concepts based on new concept discovery

This paper builds a general rumor dataset based on China’s mainstream rumor-refuting websites as data sources, and proposes a domain new concept discovery algorithm to discover domain new words in the dataset, add them to the word segmentation dictionary to improve the accuracy of word segmentation, and cluster them according to rumor type, resulting in a concept subclass dictionary based on the real rumor dataset, which provided realistic basis and data support for the conceptual design of each subclass in domain ontology.

Building a general rumor dataset

The rumor dataset constructed in this paper contains 12,472 texts, with 6236 rumors and 6236 non-rumors; the data sources are China’s mainstream internet rumor-refuting websites: 1032 from the internet rumor exposure platform of China internet joint rumor-refuting platform, 270 from today’s rumor-refuting of China internet joint rumor-refuting platform, 1852 from Tencent news Jiaozhen platform, 1744 from Baidu rumor-refuting platform, 7036 from science rumor-refuting platform, and 538 from Weibo community management center. This paper invited eight researchers to annotate the labels (rumor, non-rumor), categories (politics and military, disease prevention and treatment, social life, science and technology, nutrition and health, others) of the rumor dataset. Because data annotation is artificial and subjective, in order to ensure the effectiveness and consistency of annotation, before inviting researchers to annotate, this paper formulates annotation standards, including the screening method, trigger words and sentence break identification of rumor information and corresponding rumor information, and clearly explains and exemplifies the screening method and trigger words of rumor categories, so as to reduce the understanding differences among researchers; in view of this standard, researchers are trained in labeling to familiarize them with labeling specifications, so as to improve their labeling ability and efficiency. The method of multi-person cross-labeling is adopted when labeling, and each piece of data is independently labeled by at least two researchers. In case of conflicting labeling results, the labeling results are jointly decided by the data annotators to increase the reliability and accuracy of labeling. After labeling, multi-person cross-validation method is used to evaluate the labeling results. Each piece of data is independently verified by at least two researchers who did not participate in labeling, and conflicting labeling results are jointly decided by at least five researchers to ensure the consistency of evaluation results. Examples of the results are shown in Table 5 .

N-gram word granularity rumor text new word discovery algorithm

Existing neologism discovery algorithms are mostly based on the granularity of Chinese characters, and the time complexity of long word discovery is high and the accuracy rate is low. The algorithm’s usefulness is low, and the newly discovered words are mostly already found in general domain dictionaries. To solve these problems, this paper proposes an online rumor new word discovery algorithm based on N-gram word granularity, as shown in Fig.  1 .

Flowchart of domain new word discovery algorithm.

First, obtain the corpus to be processed \({\varvec{c}}=\{{{\varvec{s}}}_{1},{{\varvec{s}}}_{2},...,{{\varvec{s}}}_{{{\varvec{n}}}_{{\varvec{c}}}}\}\) , and perform the first preprocessing on the corpus to be processed, which includes: sentence segmentation, Chinese word segmentation and punctuation removal for the corpus to be processed. Obtain the first corpus \({{\varvec{c}}}^{{\varvec{p}}}=\{{{\varvec{s}}}_{1}^{{\varvec{p}}},{{\varvec{s}}}_{2}^{{\varvec{p}}},...,{{\varvec{s}}}_{{{\varvec{n}}}_{{\varvec{c}}}}^{{\varvec{p}}}\}\) ; where \({s}_{i}\) represents the \(i\) -th sentence in the corpus to be processed, \({n}_{c}\) represents the number of sentences in the corpus to be processed, and \({s}_{i}^{p}\) is the i-th sentence in the first corpus; perform N-gram operation on each sentence in the first corpus separately, and obtain multiple candidate words \(n=2\sim 5\) ; count the word frequency of each candidate word in the first corpus, and remove the candidate words with word frequency less than the first threshold, and obtain the first class of candidate word set;calculate the cohesion of each candidate word in the first class of candidate word set according to the following formula:

In the formula, \(P(\cdot )\) represents word frequency.Then filter according to the second threshold corresponding to N-gram operation, and obtain the second class of candidate word set; after loading the new words in the second class of candidate word set into LTP dictionary, perform the second preprocessing on the corpus to be processed \({\varvec{c}}=\{{{\varvec{s}}}_{1},{{\varvec{s}}}_{2},...,{{\varvec{s}}}_{{{\varvec{n}}}_{{\varvec{c}}}}\}\) ; and obtain the second corpus \({{\varvec{c}}}^{{\varvec{p}}\boldsymbol{^{\prime}}}=\{{{\varvec{s}}}_{1}^{{\varvec{p}}\boldsymbol{^{\prime}}},{{\varvec{s}}}_{2}^{{\varvec{p}}\boldsymbol{^{\prime}}},...,{{\varvec{s}}}_{{{\varvec{n}}}_{{\varvec{c}}}}^{{\varvec{p}}\boldsymbol{^{\prime}}}\}\) ; where the second preprocessing includes: sentence segmentation, Chinese word segmentation and stop word removal for the corpus to be processed; after obtaining the vector representation of each word in the second corpus, determine the vector representation of each new word in the second class of candidate word set; according to the vector representation of each new word, use K-means algorithm for clustering; according to the clustering results and preset classification rules, classify each new word to the corresponding domain. The examples of new words discovered are shown in Table 6 :

RoBERTa-Kmeans rumor text concepts extraction algorithm

After adding the new words obtained by the new word discovery to the LTP dictionary, the accuracy of LTP word segmentation is improved. The five types of rumor texts established in this paper are segmented by using the new LTP dictionary, and the word vectors are obtained by inputting them into the RoBERTa word embedding layer after removing the stop words. The word vectors are clustered by k-means according to rumor type to obtain the concept subclass dictionary. The main process is as follows:

(1) Word embedding layer

The RoBERTa model uses Transformer-Encode for computation, and each module contains multi-head attention mechanism, residual connection and layer normalization, feed-forward neural network. The word vectors are obtained by representing the rumor texts after accurate word segmentation through one-hot encoding, and the position encoding represents the relative or absolute position of the word in the sequence. The word embedding vectors generated by superimposing the two are used as input X. The multi-head attention mechanism uses multiple independent Attention modules to perform parallel operations on the input information, as shown in formula ( 2 ):

where \(\left\{{\varvec{Q}},{\varvec{K}},{\varvec{V}}\right\}\) is the input matrix, \({{\varvec{d}}}_{{\varvec{k}}}\) is the dimension of the input matrix. After calculation, the hidden vectors obtained after computation are residual concatenated with layer normalization, and then calculated by two fully connected layers of feed-forward neural network for input, as shown in formula ( 3 ):

where \(\left\{{{\varvec{W}}}_{{\varvec{e}}},{{\varvec{W}}}_{0}\boldsymbol{^{\prime}}\right\}\) are the weight matrices of two connected layers, \(\left\{{{\varvec{b}}}_{{\varvec{e}}},{{\varvec{b}}}_{0}\boldsymbol{^{\prime}}\right\}\) are the bias terms of two connected layers.

After calculation, a bidirectional association between word embedding vectors is established, which enables the model to learn the semantic features contained in each word embedding vector in different contexts. Through fine-tuning, the learned knowledge is transferred to the downstream clustering task.

(2) K-means clustering

Randomly select k initial points to obtain k classes, and iterate until the loss function of the clustering result is minimized. The loss function can be defined as the sum of squared errors of each sample point from its cluster center point, as shown in formula ( 4 ).

where \({x}_{i}\) represents the \(i\) sample, \({a}_{i}\) is the cluster that \({x}_{i}\) belongs to, \({u}_{{a}_{i}}\) represents the corresponding center point, \(N\) is the total number of samples.

After RoBERTa-kmeans calculation, the concept subclasses obtained are manually screened, merged repetition items, deleted invalid items, and finally obtained 79 rumor concept subclasses, including 14 politics and military subclasses, 23 disease prevention and treatment subclasses, 15 social life subclasses, 13 science and technology subclasses, and 14 nutrition and health subclasses. Some statistics are shown in Table 7 .

Each concept subclass is obtained by clustering several topic words. For example, the topic words that constitute the subclasses of body part, epidemic prevention and control, chemical drugs, etc. under the disease prevention and treatment topic are shown in Table 8 .

(3) Determining the terminology set

This paper constructs a three-dimensional rumor domain ontology terminology set based on the above three methods, and unifies the naming of the terms. Some of the terms are shown in Table 9 .

Framework layer construction

Define core classes and hierarchy, define parent classes.

This paper aims at fine-grained hierarchical modeling of the relationship between the content characteristics of multi-domain network rumors. Therefore, the top-level parent class needs to include the rumor category and the main content characteristics of a sub-category rumor design. The main content characteristics are the clustering results of domain concepts extracted based on the content characteristics of core documents, that is, rumor text feature, rumor emotional characteristic, rumor credibility and social context. The specific contents of the five top parent classes are as follows:

Rumor type: the specific classification of rumors under different subject categories; Rumor text feature, the common features of rumor texts in terms of theme, style, rhetoric, etc. Rumor emotional characteristic: the emotional elements of rumor texts, the Rumor motive of the publisher, and the emotional changes they hope to trigger in the receiver. Rumor credibility: the authority of the information source, the credibility of the evidence material provided by the publisher, and the effectiveness of the testimony method. Social context: the relevant issues and events in the society when the rumor is published.

Induce subclasses and design hierarchical relationships

In this paper, under the top-level parent class, according to the top-level concepts of top-level ontologies such as SUMO, senticnet and ERE and their subclass structures, and the rumor text features of each category extracted from the real rumor text dataset, we summarize its 88 subclasses and design the hierarchical relationships, as shown in Fig.  2 , which include:

(1) Rumor text feature

Diagram of the core classes and hierarchy of the rumor domain ontology.

① Text theme 6 , 8 , 13 , 18 , 53 : the theme or topic that the rumor text content involves. Based on the self-built rumor dataset, it is divided into politics and military 54 , involving information such as political figures, political policies, political relations, political activities, military actions, military events, strategic objectives, politics and military reviews, etc.; nutrition and health 55 , involving information such as the relationship between human health and nutrition, the nutritional components and value of food, the plan and advice for healthy eating, health problems and habits, etc.; disease prevention and treatment 10 , involving information such as the definition of disease, vaccine, treatment, prevention, data, etc.; social life 56 , involving information such as social issues, social environment, social values, cultural activities, social media, education system, etc.; science and technology 57 , involving information such as scientific research, scientific discovery, technological innovation, technological application, technological enterprise, etc.; other categories.

② Text element 15 : the structured information of the rumor text contents. It is divided into character, political character, public character, etc.; geographical position, city, region, area, etc.; event, historical event, current event, crisis event, policy event, etc.; action, protection, prevention and control, exercise, fighting, crime, eating, breeding, health preservation, rest, exercise, education, sports, social, cultural, ideological, business, economic, transportation, etc.; material, food, products (food, medicine, health products, cosmetics, etc.) and the materials they contain and their relationship with human health. effect, nutrition, health, harm, natural disaster, man-made disaster, guarantee, prevention, treatment, etc.; institution, government, enterprise, school, hospital, army, police, social group, etc.; nature, weather, astronomy, environment, agriculture, disease, etc.

③ Text style 7 , 10 : the discourse style of the rumor text contents, preferring exaggerated and emotional expression. It is divided into gossip style, creating conflict or entertainment effect; curious style, satisfying people’s curiosity and stimulation; critical style, using receivers’ stereotypes or preconceptions; lyrical style, creating resonance and influencing emotion; didactic style influencing receivers’ thought and behavior from an authoritative perspective; plain style concise objective arousing resonance etc.

④ Text feature 7 , 58 : special language means in the rumor text contents that can increase the transmission and influence of the rumor. It is divided into extensive punctuation reminding or attracting receivers’ attention; many mood words enhancing emotional color and persuasiveness; many emoji conveying attitude; induce forwarding using @ symbol etc. to induce receivers to forward etc.

⑤ Text rhetoric 15 : common rhetorical devices in rumor contents. It is divided into metaphor hyperbole repetition personification etc.

(2) Rumor emotional characteristic

① Emotion category 17 , 59 , 60 : the emotional tendency and intensity expressed in the rumor texts. It is divided into positive emotion happy praise etc.; negative emotion fear 10 anger sadness anxiety 61 dissatisfaction depression etc.; neutral emotion no preference plain objective etc.

② Emotional appeal 16 , 62 , 63 : the online rumor disseminator hopes that the rumor they disseminate can trigger some emotional changes in the receiver. It is divided into “joy” happy pleasant satisfied emotions that prompt receivers to spread or believe some rumors that are conducive to social harmony; “love” love appreciation admiration emotions that prompt receivers to spread or believe some rumors that are conducive to some people or group interests; “anger” angry annoyed dissatisfied emotions that prompt receivers to spread or believe some rumors that are anti-social or intensify conflicts; “fear” fearful afraid nervous emotions that prompt receivers to spread or believe some rumors that have bad effects deliberately exaggerated; “repugnance” disgusted nauseous emotions that prompt receivers to spread or believe some rumors that are detrimental to social harmony; “surprise” surprised shocked amazed emotions that prompt receivers to spread or believe some rumors that deliberately attract traffic exaggerated fabricated etc.

③ Rumor motive 17 , 64 , 65 , 66 : the purpose and need of the rumor publisher to publish rumors and the receiver to forward rumors. Such as profit-driven seeking fame and fortune deceiving receivers; emotional catharsis relieving dissatisfaction emotions by venting; creating panic creating social unrest and riots disrupting social order; entertainment fooling receivers seeking stimulation; information verification digging out the truth of events etc.

(3) Rumor credibility

① source credibility 7 , 17 : the degree of trustworthiness that the information source has. Such as official institutions and authoritative experts and scholars in the field with high credibility; well-known encyclopedias and large-scale civil organizations with medium credibility; small-scale civil organizations and personal hearsay personal experience with low credibility etc.

② evidence credibility 61 : the credibility of the information proof material provided by the publisher. Data support such as scientific basis based on scientific theory or method; related feature with definite research or investigation result in data support; temporal background with clear time place character event and other elements which related to the information content; the common sense of life in line with the facts and scientific common sense that are widely recognized.

③ testimony method 10 , 11 , 17 : the method to support or refute a certain point of view. Such as multimedia material expressing or fabricating content details through pictures videos audio; authority endorsement policy documents research papers etc. of authorized institutions or persons; social identity identity of social relation groups.

(4) Social context

① social issue 67 : some bad phenomena or difficulties in society such as poverty pollution corruption crime government credibility decline 68 etc.

② public attention 63 : events or topics that arouse widespread attention or discussion in the society such as sports events technological innovation food safety religious beliefs Myanmar fraud nuclear wastewater discharge etc.

③ emergency(public sentiment) 69 : some major or urgent events that suddenly occur in society such as earthquake flood public safety malignant infectious disease outbreaks etc.

(5) Rumor type

① Political and military rumor:

Political image rumor: rumors related to images closely connected to politics and military, such as countries, political figures, institutions, symbols, etc. These include positive political image smear rumor, negative political image whitewash rumor, political image fabrication and distortion rumor, etc.

Political event rumor: rumors about military and political events, such as international relations, security cooperation, military strategy, judicial trial, etc. These include positive political event smear rumor, negative political event whitewash rumor, political event fabrication and distortion rumor, etc.

② Nutrition and health rumor:

Food product rumor: rumors related to food, products (food, medicine, health products, cosmetics, etc.), the materials they contain and their association with human health. These include positive effect of food product rumor, negative effect of food product rumor, food product knowledge rumor, etc.

Living habit rumor: rumors related to habitual actions in life and their association with human health. These include positive effect of living habit rumor, negative effect of living habit rumor, living habit knowledge rumor, etc.

③ Disease prevention and treatment rumor:

Disease management rumor: rumors related to disease management and control methods that maintain and promote individual and group health. These include positive prevention and treatment rumor, negative aggravating disease rumor, disease management knowledge rumor, etc.

Disease confirmed transmission rumor: rumors about the confirmation, transmission, and immunity of epidemic diseases at the social level in terms of causes, processes, results, etc. These include local confirmed cases rumor, celebrity confirmed cases rumor, transmission mechanism rumor, etc.

Disease notification and advice rumor: rumors that fabricate or distort the statements of authorized institutions or experts in the field, and provide false policies or suggestions related to diseases. These include institutional notification rumor, expert advice rumor, etc.

④ Social life rumor:

Public figure public opinion rumor: rumors related to public figures’ opinions, actions, private lives, etc. These include positive public figure smear rumor, negative public figure whitewash rumor, public figure life exposure rumor, etc.

Social life event rumor: rumors related to events, actions, and impacts on people's social life. These include positive event sharing rumor, negative event exposure rumor, neutral event knowledge rumor, etc.

Disaster occurrence rumor: rumors related to natural disasters or man-made disasters and their subsequent developments. These include natural disaster occurrence rumor, man-made disaster occurrence rumor, etc.

⑤ Science and technology rumor:

Scientific knowledge rumor: rumors related to natural science or social science theories and knowledge. These include scientific theory rumor, scientific concept rumor, etc.

Science and technology application rumor: rumors related to the research and development and practical application of science and technology and related products. These include scientific and technological product rumor, scientific and technological information rumor, etc.

⑥ Other rumor: rumors that do not contain elements from the above categories.

Definition of core properties and facets of properties

Properties in the ontology are used to describe the relationships between entities or the characteristics of entities. Object properties are relationships that connect two entities, describing the interactions between entities; data properties represent the characteristics of entities, usually in the form of some data type. Based on the self-built rumor dataset, this paper designs object properties, data properties and facets of properties for the parent classes and subclasses of the rumor domain ontology.

Object properties

A partial set of object properties is shown in Table 10 .

Data attributes

The partial data attribute set is shown in Table 11 .

Creating instances

Based on the defined core classes and properties, this paper creates instances according to the real rumor dataset. An example is shown in Table 12 .

This paper selects the online rumor that “Lin Chi-ling was abused by her husband Kuroki Meisa, the tears of betrayal, the shadow of gambling, all shrouded her head. Even if she tried to divorce, she could not get a solution…..” as an example, and draws a structure diagram of the rumor domain ontology instance, as shown in Fig.  3 . This instance shows the seven major text features of the rumor text: text theme, text element, text style, emotion category, emotional appeal, rumor motivation, and rumor credibility, as well as the related subclass instances, laying a foundation for building a multi-source rumor domain knowledge graph.

Schematic example of the rumor domain ontology.

Encoding ontology and visualization

Encoding ontology.

This paper uses OWL language to encode the rumor domain ontology, to accurately describe the entities, concepts and their relationships, and to facilitate knowledge reasoning and semantic understanding. Classes in the rumor domain ontology are represented by the class “Class” in OWL and the hierarchical relationship is represented by subclassof. For example, in the creation of the rumor emotional characteristic class and its subclasses, the OWL code is shown in Fig.  4 :

Partial OWL codes of the rumor domain ontology.

The ontology is formalized and stored as a code file using the above OWL language, providing support for reasoning.

Ontology visualization

This paper uses protégé5.5 to visualize the rumor domain ontology, showing the hierarchical structure and relationship of the ontology parent class and its subclasses. Due to space limitations, this paper only shows the ontology parent class “RumorEmotionalFeatures” and its subclasses, as shown in Fig.  5 .

Ontology parent class “RumorEmotionalFeatures” and its subclasses.

Ontology reasoning and validation

Swrl reasoning rule construction.

SWRL reasoning rule is an ontology-based rule language that can be used to define Horn-like rules to enhance the reasoning and expressive ability of the ontology. This paper uses SWRL reasoning rules to deal with the conflict relationships between classes and between classes and instances in the rumor domain ontology, and uses pellet reasoner to deeply mine the implicit semantic relationships between classes and instances, to verify the semantic parsing ability and consistency of the rumor domain ontology.

This paper summarizes the object property features of various types of online rumors based on the self-built rumor dataset, maps the real rumor texts with the rumor domain ontology, constructs typical SWRL reasoning rules for judging 32 typical rumor types, as shown in Table 13 , and imports them into the protégé rule library, as shown in Fig.  6 . In which x, n, e, z, i, t, v, l, etc. are instances of rumor types, text theme, emotion category, effect, institution, event, action, geographical position, etc. in the ontology. HasTheme, HasEmotion, HasElement, HasSource, HasMood and HasSupport are object property relationships. Polarity value is a data property relationship.

Partial SWRL rules for the rumor domain ontology.

Implicit knowledge mining and verification based on pellet reasoner

This paper extracts corresponding instances from the rumor dataset, imports the rumor domain ontology and SWRL rule description into the pellet reasoner in the protégé software, performs implicit knowledge mining of the rumor domain ontology, judges the rumor type of the instance, and verifies the semantic parsing ability and consistency of the ontology.

Positive prevention and treatment of disease rumors are mainly based on the theme of disease prevention and treatment, usually containing products to be sold (including drugs, vaccines, equipment, etc.) and effect of disease names, claiming to have positive effects (such as prevention, cure, relief, etc.) on certain diseases or symptoms, causing positive emotions such as surprise and happiness among patients and their families, thereby achieving the purpose of selling products. The text features and emotional features of this kind of rumors are relatively clear, so this paper takes the rumor text “Hong Kong MDX Medical Group released the ‘DCV Cancer Vaccine’, which can prevent more than 12 kinds of cancers, including prostate cancer, breast cancer and lung cancer.” as an example to verify the semantic parsing ability of the rumor domain ontology. The analysis result of this instance is shown in Fig.  7 . The text theme is cancer prevention in disease prevention and treatment, the text style is plain narrative style, and the text element includes product-DCV cancer vaccine, positive effect-prevention, disease name-prostate cancer, disease name-breast cancer, disease name-lung cancer; the emotion category of this instance is a positive emotion, emotional appeal is joy, love, surprise; The motive for releasing rumors is profit-driven in selling products, the information source is Hong Kong MDX medical group, and pictures and celebrity endorsements are used as testimony method. This paper uses a pellet reasoner to reason on the parsed instance based on SWRL rules, and mines out the specific rumor type of this instance as positive prevention and treatment of disease rumor. This paper also conducted similar instance analysis and reasoning verification for other types of rumor texts, and the results show that the ontology has high consistency and reliability.

Implicit relationship between rumor instance parsing results and pellet reasoner mining.

Comparison and evaluation of ontology performance

In this paper, the constructed ontology is compared with the representative rumor index system in the field. By inviting four experts to make a comprehensive evaluation based on the self-built index system 70 , 71 , 72 , their performance in the indicators of reliability, coverage and operability is evaluated. According to the ranking order given by experts, they are given 1–4 points, and the first place in each indicator item gets four points. The average value given by three experts is taken as the single indicator score of each subject, and the total score of each indicator item is taken as the final score of the subject.

As can be seen from Table 14 , the rumor domain ontology constructed in this paper constructs a term set through three ways: reusing the existing ontology, extracting the content features of core documents and discovering new concepts based on real rumor data sets, and the ontology structure has been verified by SWRL rule reasoning of pellet inference machine, which has high reliability; ontology covers six kinds of Chinese online rumors, including the grammatical, semantic, pragmatic and social characteristics of rumor text characteristics, emotional characteristics, rumor credibility and social background, which has a high coverage; ontology is coded by OWL language specification and displayed visually on protege, which is convenient for further expansion and reuse of scholars and has high operability.

The construction method of TFI domain ontology proposed in this paper includes terminology layer, framework layer and instance layer. Compared with the traditional methods, this paper adopts three-dimensional data set construction method in terminology layer construction, investigates top-level ontology and related core documents, and completes the mapping of reusable top-level ontology from top to bottom and the concept extraction of rumor content features in existing literature research. Based on the mainstream internet rumor websites in China, the authoritative real rumor data set is established, and the new word discovery algorithm of N-gram combined with RoBERTa-Kmeans clustering algorithm is used to automatically discover new concepts in the field from bottom to top; determine the terminology set of domain ontology more comprehensively and efficiently. This paper extracts the clustering results of domain concepts based on the content characteristics of core documents in the selection of parent rumors content characteristics in the framework layer construction, that is, rumors text characteristics, rumors emotional characteristics, rumors credibility characteristics and social background characteristics; based on the emotional characteristics and the entity categories of real rumor data sets, the characteristics of rumor categories are defined. Sub-category rumor content features combine the concept of three-dimensional rumor term set and the concept distribution based on real rumor data set, define the sub-category concept and hierarchical relationship close to the real needs, and realize the fine-grained hierarchical modeling of the relationship between multi-domain network rumor content features. In this paper, OWL language is used to encode the rumor domain ontology in the instance layer construction, and SWRL rule language and Pellet inference machine are used to deal with the conflict and mine tacit knowledge, judge the fine-grained categories of rumor texts, and realize the effective quality evaluation of rumor ontology. This makes the rumor domain ontology constructed in this paper have high consistency and reliability, and can effectively analyze and reason different types of rumor texts, which enriches the knowledge system in this field and provides a solid foundation for subsequent credible rumor detection and governance.

However, the study of the text has the following limitations and deficiencies:

(1) The rumor domain ontology constructed in this paper only considers the content characteristics, but does not consider the user characteristics and communication characteristics. User characteristics and communication characteristics are important factors affecting the emergence and spread of online rumors, and the motivation and influence of rumors can be analyzed. In this paper, these factors are not included in the rumor feature system, which may limit the expressive ability and reasoning ability of the rumor ontology and fail to fully reflect the complexity and multidimensional nature of online rumors.

(2) In this paper, the mainstream Internet rumor-dispelling websites in China are taken as the data source of ontology instantiation. The data covers five rumor categories: political and military, disease prevention, social life, science and technology, and nutrition and health, and the data range is limited. And these data sources are mainly official or authoritative rumor websites, and their data volume and update frequency may not be enough to reflect the diversity and variability of online rumors, and can not fully guarantee the timeliness and comprehensiveness of rumor data.

(3) The SWRL reasoning rules used in this paper are based on manual writing, which may not cover all reasoning scenarios, and the degree of automation needs to be improved. The pellet inference engine used in this paper is an ontology inference engine based on OWL-DL, which may have some computational complexity problems and lack of advanced reasoning ability.

The following aspects can be considered for optimization and improvement in the future:

(1) This paper will introduce user characteristics into the rumor ontology, and analyze the factors that cause and accept rumors, such as social attributes, psychological state, knowledge level, beliefs and attitudes, behavioral intentions and so on. This paper will introduce the characteristics of communication, and analyze the propagation dynamic factors of various types of rumors, such as propagation path, propagation speed, propagation range, propagation period, propagation effect, etc. This paper hopes to introduce these factors into the rumor feature system, increase the breadth and depth of the rumor domain ontology, and provide more credible clues and basis for the detection, intervention and prevention of rumors.

(2) This paper will expand the data sources, collect the original rumor data directly from social media, news media, authoritative rumor dispelling institutions and other channels, and build a rumor data set with comprehensive types, diverse expressions and rich characteristics; regularly grab the latest rumor data from these data sources and update and improve the rumor data set in time; strengthen the expressive ability of rumor ontology instance layer, and provide full data support and verification for the effective application of ontology.

(3) The text will introduce GPT, LLaMA, ChantGLM and other language models, and explore the automatic generation algorithm and technology of ontology inference rules based on rumor ontology and dynamic Prompt, so as to realize more effective and intelligent rumor ontology evaluation and complex reasoning.

This paper proposed a method of constructing TFI network rumor domain ontology. Based on the concept distribution of three-dimensional term set and real rumor data set, the main features of network rumors are defined, including text features, emotional features, credibility features, social background features and category features, and the relationships among these multi-domain features are modeled in a fine-grained hierarchy, including five parent classes and 88 subcategories. At the instance level, 32 types of typical rumor category judgment and reasoning rules are constructed, and the ontology is processed by using SWRL rule language and pellet inference machine for conflict processing and tacit knowledge mining, so that the semantic analysis and reasoning of rumor text content are realized, which proves its effectiveness in dealing with complex, fuzzy and uncertain information in online rumors and provides a new perspective and tool for the interpretable analysis and processing of online rumors.

Data availability

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

Jiang, S. The production scene and content characteristics of scientific rumors. Youth J. https://doi.org/10.15997/j.cnki.qnjz.2020.33.011 (2020).

Article   Google Scholar  

Jin, X. & Zhao, Y. Analysis of internet rumors from the perspective of co-governance—Practice of rumor governance on wechat platform. News and Writing. 6 , 41–44 (2017).

Bai, S. Research on the causes and countermeasures of internet rumors. Press https://doi.org/10.15897/j.cnki.cn51-1046/g2.2010.04.035 (2010).

Garg, S. & Sharma, D. K. Linguistic features based framework for automatic fake news detection. Comput. Ind. Eng. 172 , 108432 (2022).

Zhao, J., Fu, C. & Kang, X. Content characteristics predict the putative authenticity of COVID-19 rumors. Front. Public Health 10 , 920103 (2022).

Article   PubMed   PubMed Central   Google Scholar  

Zhang, Z., Shu, K. & He, L. The theme and characteristics of wechat rumors. News and Writing. 1 , 60–64 (2016).

Li, B. & Yu, G. Research on the discourse space and communication field of internet rumors in the post-truth era—Based on the analysis of 4160 rumors in wechat circle of friends. Journalism Research. 2 , 103–112 (2018).

Yu, G. Text structure and expression characteristics of internet rumors—Analysis of 6000+ rumors based on tencent big data screening and identification. News and Writing. 2 , 53–59 (2018).

Mourão, R. R. & Robertson, C. T. Fake news as discursive integration: An analysis of sites that publish false, misleading, hyperpartisan and sensational information. J. Stud. 20 , 2077–2095 (2019).

Google Scholar  

Zhou, G. Analysis on the content characteristics and strategies of epidemic rumors—Based on Sina’s “novel coronavirus epidemic rumors list”. Sci. Popul. https://doi.org/10.19293/j.cnki.1673-8357.2021.05.002 (2021).

Huang, Y. An analysis of the internal logic and methods of rumor “confirmation”—An empirical study based on 60 rumors spread on wechat. J. Party Sch. Tianjin Munic. Comm. CPC 20 , 7 (2018).

Butt, S. et al . What goes on inside rumour and non-rumour tweets and their reactions: A psycholinguistic analyses. Comput. Hum. Behav. 135 , 107345 (2022).

Zhou, L., Tao, J. & Zhang, D. Does fake news in different languages tell the same story? An analysis of multi-level thematic and emotional characteristics of news about COVID-19. Inf. Syst. Front. 25 , 493–512. https://doi.org/10.1007/s10796-022-10329-7 (2023).

Article   PubMed   Google Scholar  

Tan, L. et al . Research status of deep learning methods for rumor detection. Multimed. Tools Appl. 82 , 2941–2982 (2023).

Damstra, A. et al. What does fake look like? A review of the literature on intentional deception in the news and on social media. J. Stud. 22 , 1947–1963. https://doi.org/10.1080/1461670X.2021.1979423 (2021).

Lai, S. & Tang, X. Research on the influence of information emotionality on the spread of online rumors. J. Inf. 35 , 116–121 (2016).

ADS   Google Scholar  

Yuan, H. & Xie, Y. Research on the rumor maker of internet rumors about public events—Based on the content analysis of 118 influential Internet rumors about public events. Journalist https://doi.org/10.16057/j.cnki.31-1171/g2.2015.05.008 (2015).

Ruan, Z. & Yin, L. Types and discourse focus of weibo rumors—Based on the content analysis of 307 weibo rumors. Contemporary Communication. 4 , 77–78+84 (2014).

Zhang, W. & Zhu, Q. Research on the Construction Method of Domain Ontology. Books and Information. 5 , 16–19+40 (2011).

Tham, K.D., Fox, M.S. & Gruninger, M. A cost ontology for enterprise modelling. In Proceedings of 3rd IEEE Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises. IEEE , 197–210. https://doi.org/10.1109/ENABL.1994.330502 (1994).

Uschold, M. & Gruninger, M. Ontologies: Principles, methods and applications. Knowl. Eng. Rev. 11 , 93–136 (1996).

Menzel, C. P., Mayer, R. J. & Painter, M. K. IDEF5 ontology description capture method: Concepts and formal foundations (Armstrong Laboratory, Air Force Materiel Command, Wright-Patterson Air Force, 1992).

Book   Google Scholar  

Song, Z., Zhu, F. & ZHANG, D. Research on air and missile defense domain ontology development based on IDEF5 and OWL. Journal of Projectiles, Rockets, Missiles and Guidance. 30 , 176–178 (2010).

Fernández-López, M., Gómez-Pérez, A. & Juristo, N. Methontology: From ontological art towards ontological engineering. AAAI-97 Spring Symposium Series . https://oa.upm.es/5484/ (1997).

Sawsaa, A. & Lu, J. Building information science ontology (OIS) with methontology and protégé. J. Internet Technol. Secur. Trans. 1 , 100–109 (2012).

Yue, L. & Liu, W. Comparative study on the construction methods of domain ontology at home and abroad. Inf. Stud. Theory Appl. 39 , 119–125. https://doi.org/10.16353/j.cnki.1000-7490.2016.08.024 (2016).

Noy, N.F. & McGuinness, D.L. Ontology development 101: A guide to creating your first ontology. Stanford knowledge systems laboratory technical report. KSL-01–05 (2001).

Luo, Y. et al . vim: Research on OWL-based vocabulary ontology construction method for units of measurement. Electronics 12 , 3783 (2023).

Al-Aswadi, F. N., Chan, H. Y. & Gan, K. H. Automatic ontology construction from text: A review from shallow to deep learning trend. Artif. Intell. Rev. 53 , 3901–3928 (2020).

Chen, X. & Mao, T. Ontology construction of documentary heritage—Taking China archives documentary heritage list as an example. Libr. Trib. 43 , 120–131 (2023).

CAS   Google Scholar  

Zhao, X. & Li, T. Research on the ontology construction of archives oriented to digital humanism—Taking Wanli tea ceremony archives as an example. Inf. Stud. Theory Appl. 45 , 154–161. https://doi.org/10.16353/j.cnki.1000-7490.2022.08.021 (2022).

Huang, X. et al . Construction of special knowledge base of government website pages based on domain ontology—Taking “COVID-19 vaccine science popularization” as an example. Libr. Inf. Serv. 66 , 35–46. https://doi.org/10.13266/j.issn.0252-3116.2022.17.004 (2022).

Jindal, R., Seeja, K. & Jain, S. Construction of domain ontology utilizing formal concept analysis and social media analytics. Int. J. Cogn. Comput. Eng. 1 , 62–69 (2020).

Ran, J. et al . Research on ontology construction of idioms and allusions based on OWL. Comput. Technol. Dev. 20 , 63–66 (2010).

Li, L. et al . Research on business process modeling of army equipment maintenance support based on IDEF5. Technol. Innov. Appl. 11 , 80–82 (2021).

Song, Z. et al . Ontology modeling of air defense and anti-missile operation process based on IDEF5/OWL. J. Missiles Guid. 30 , 176–178 (2010).

Li, A., Xu, Y. & Chi, Y. Summary of ontology construction and application. Inf. Stud. Theory Appl 46 , 189–195. https://doi.org/10.16353/j.cnki.1000-7490.2023.11.024 (2023).

Yang, J., Song, C. & Jin, L. Ontology construction of emergency plan based on methontology method. J. Saf. Environ. 18 , 1427–1431. https://doi.org/10.13637/j.issn.1009-6094.2018.04.033 (2018).

Duan, L. & Li, H. Ontology modeling method of high-resolution image rural residential area supported by OIA technology. Modern Agricultural Science and Technology. 2 , 338–340 (2016).

Chen, Y. & Jiang, H. Construction of fire inspection knowledge map based on GIS geospatial relationship. J. Subtrop. Resour. Environ. 18 , 109–118. https://doi.org/10.19687/j.cnki.1673-7105.2023.03.014 (2023).

Zhu, L. et al. Construction of TCM asthma domain ontology. Chin. J. Exp. Tradit. Med. Formulae 23 , 222–226. https://doi.org/10.13422/j.cnki.syfjx.2017150222 (2017).

Li, H. et al . Domain ontology construction and relational reasoning. J. Inf. Eng. Univ. 24 , 321–327 (2023).

Zhang, Y. et al. Construction of ontology of stroke nursing field based on corpus. Chin. Nurs. Res. 36 , 4186–4190 (2022).

Wu, M. et al. Ontology construction of natural gas market knowledge map. Pet. New Energy 34 , 71–76 (2022).

Li, X. et al . Research on ontology construction based on thesaurus and its semantic relationship. Inf. Sci. 36 , 83–87 (2018).

Article   ADS   CAS   Google Scholar  

Chen, Q. et al . Construction of knowledge ontology of clinical trial literature of traditional Chinese medicine. Chin. J. Exp. Tradit. Med. Formulae 29 , 190–197. https://doi.org/10.13422/j.cnki.syfjx.20231115 (2023).

Xiao, Y. et al. Construction and application of novel coronavirus domain ontology. Mil. Med. 46 , 263–268 (2022).

Su, N. et al . Automatic construction method of domain-limited ontology. Lifting the Transport Machinery. 8 , 49–57 (2023).

Zheng, S. et al . Ontology construction method for user-generated content. Inf. Sci. 37 , 43–47. https://doi.org/10.13833/j.issn.1007-7634.2019.11.007 (2019).

Dong, J., Wang, J. & Wang, Z. Ontology automatic construction method for human-machine-object ternary data fusion in manufacturing field. Control Decis. 37 , 1251–1257. https://doi.org/10.13195/j.kzyjc.2020.1298 (2022).

Zhu, L., Hua, G. & Gao, W. Mapping ontology vertices to a line using hypergraph framework. Int. J. Cogn. Comput. Eng. 1 , 1–8 (2020).

Zhai, Y. & Wang, F. Research on the construction method of Chinese domain ontology based on text mining. Inf. Sci. 33 , 3–10. https://doi.org/10.13833/j.cnki.is.2015.06.001 (2015).

Duan, Z. Generation mechanism of internet rumors and countermeasures. Guizhou Soc. Sci. https://doi.org/10.13713/j.cnki.cssci.2016.04.014 (2016).

Du, Z. & Zhi, S. The harm and governance of network political rumors. Academic Journal of Zhongzhou. 4 , 161–165 (2019).

Song, X. et al . Research on influencing factors of health rumor sharing willingness based on MOA theory. J. China Soc. Sci. Tech. Inf. 39 , 511–520 (2020).

Jiang, S. Research on the characteristics, causes and countermeasures of social rumors dissemination in china in recent years. Red Flag Manuscript . 16 , 4 (2011).

Huang, J., Wang, G. & Zhong, S. Research on the propagation law and function mode of sci-tech rumors. Journal of Information. 34 , 156–160 (2015).

Liu, Y. et al . A survey of rumor recognition in social media. Chin. J. Comput. 41 , 1536–1558 (2018).

Wei, D. et al. Public emotions and rumors spread during the covid-19 epidemic in China: Web-based correlation study. J. Med. Internet Res. 22 , e21933 (2020).

Runxi, Z. & Di, Z. A model and simulation of the emotional contagion of netizens in the process of rumor refutation. Sci. Rep. https://doi.org/10.1038/s41598-019-50770-4 (2019).

Tang, X. & Lai, S. Research on the forwarding of network health rumors in public health security incidents—Interaction between perceived risk and information credibility. J. Inf. 40 , 101–107 (2021).

Nicolas, P., Dominik, B. & Stefan, F. Emotions in online rumor diffusion. EPJ Data Sci. https://doi.org/10.1140/epjds/s13688-021-00307-5 (2021).

Deng, G. & Tang, G. Research on the spread of network rumors and its social impact. Seeker https://doi.org/10.16059/j.cnki.cn43-1008/c.2005.10.031 (2005).

Ji, Y. Research on the communication motivation of wechat rumors. Youth J. https://doi.org/10.15997/j.cnki.qnjz.2019.17.006 (2019).

Yuan, G. Analysis on the causes and motives of internet rumors in emergencies—Taking social media as an example. Media. 21 , 80–83 (2016).

Zhao, N., Li, Y. & Zhang, J. A review of the research on influencing factors and motivation mechanism of rumor spread. J. Psychol. Sci. 36 , 965–970. https://doi.org/10.16719/j.cnki.1671-6981.2013.04.015 (2013).

Article   CAS   Google Scholar  

Hu, H. On the formation mechanism of social rumors from the perspective of “rumors and salt storm”. J. Henan Univ. 52 , 63–68 (2012).

Yue, Y. et al. Trust in government buffers the negative effect of rumor exposure on people’s emotions. Curr. Psychol. 42 , 23917–23930 (2023).

Wang, C. & Hou, X. Analysis of rumor discourse in major emergencies. J. Commun. 19 , 34–38 (2012).

Xu, L. Research progress of ontology evaluation. J. China Soc. Scie. Tech. Inf. 35 , 772–784 (2016).

Lantow, B. & Sandkuhl, K. An analysis of applicability using quality metrics for ontologies on ontology design patterns. Intell. Syst. Acc. Financ. Manag. 22 , 81–99 (2015).

Pak, J. & Zhou, L. A framework for ontology evaluationIn. Exploring the Grand Challenges for Next Generation E-Business: 8th Workshop on E-Business, WEB 2009, Phoenix, AZ, USA, December 15, 2009, Revised Selected Papers 8. , 10–18. https://doi.org/10.1007/978-3-642-17449-0_2 (Springer Berlin Heidelberg, 2011).

Download references

Acknowledgements

This study was financially supported by Xi'an Major Scientific and Technological Achievements Transformation and Industrialization Project (20KYPT0003-10).

This work was supported by Xi’an Municipal Bureau of Science and Technology, 20KYPT0003-10.

Author information

Authors and affiliations.

School of Economics and Management, Xidian University, 266 Xifeng Road, Xi’an, 710071, China

Jianbo Zhao, Huailiang Liu, Weili Zhang, Tong Sun, Qiuyi Chen, Yan Zhuang, Xiaojin Zhang & Shanzhuang Zhang

School of Artificial Intelligence, Xidian University, 266 Xifeng Road, Xi’an, 710071, China

Yuehai Wang, Jiale Cheng & Ruiyu Ding

School of Telecommunications Engineering, Xidian University, 266 Xifeng Road, Xi’an, 710071, China

You can also search for this author in PubMed   Google Scholar

Contributions

H.L. formulated the overall research strategy and guided the work. J.Z kept the original data on which the paper was based and verified whether the charts and conclusions accurately reflected the collected data. J.Z. W.Z. and T.S. wrote the main manuscript text. W.Z. Y.W. and Q.C. finished collecting and sorting out the data. J.C. Y.Z. and X.Z. prepared Figs.  1 – 7 , S.Z. B.L. and R.D. prepared Tables 1 – 14 . All authors reviewed the manuscript.

Corresponding author

Correspondence to Jianbo Zhao .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

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

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Zhao, J., Liu, H., Zhang, W. et al. Research on domain ontology construction based on the content features of online rumors. Sci Rep 14 , 12134 (2024). https://doi.org/10.1038/s41598-024-62459-4

Download citation

Received : 07 December 2023

Accepted : 16 May 2024

Published : 27 May 2024

DOI : https://doi.org/10.1038/s41598-024-62459-4

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

• Rumor content features
• Domain ontology
• Top-level ontology reuse
• New concept discovery

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.