media bias case study

Media Bias Case Studies

• First Online: 03 March 2022

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• Kejo Starosta   ORCID: orcid.org/0000-0002-3815-4238 5  

Part of the book series: Sustainable Management, Wertschöpfung und Effizienz ((SMWE))

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The previous case studies have shown that biases in the news might arise from news coverage and that lower news coverage might lead to less objective news. The following case studies seek further evidence for this working hypothesis and aim to exploit this finding to detect biased, tendentious, and probably fake news.

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Starosta, K. (2022). Media Bias Case Studies. In: Measuring the Impact of Online Media on Consumers, Businesses and Society. Sustainable Management, Wertschöpfung und Effizienz. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-36729-9_9

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Study of headlines shows media bias is growing

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Researchers used machine learning to uncover media bias in publications across the political spectrum.

News stories about domestic politics and social issues are becoming increasingly polarized along ideological lines according to a study of 1.8 million news headlines from major US news outlets from 2014 to 2022. A team from the University of Rochester led by Jiebo Luo , a professor of computer science and the Albert Arendt Hopeman Professor of Engineering, used machine learning to analyze headlines and presented their findings about growing media bias at the MEDIATE workshop of the International AAAI Conference on Web and Social Media .

The researchers said that while there is broad consensus that news media outlets adopt ideological perspectives in their articles, previous studies dissecting the differences among outlets were limited in scope and used small sample sizes. Machine-learning techniques allowed the researchers to study a vast sample of headlines over an eight-year period across nine representative media outlets including the New York Times, Bloomberg, CNN, NBC, the Wall Street Journal, Christian Science Monitor, the Federalist, Reason, and the Washington Times.

The study used a technique called multiple correspondence analysis to measure the fine-grained thematic discrepancies among headlines. The researchers grouped the stories into four categories—domestic politics, economic issues, social issues, and foreign affairs—and analyzed how left, right, and central media outlets differed in the language they used in their headlines.

The team observed that US media outlets across the political spectrum were consistent and similar in covering economic issues. While they found discrepancies in reporting foreign affairs, they attributed that to diversity in individual journalistic styles. For example, the authors say the Wall Street Journal and Bloomberg primarily concentrate on the economic and financial implications of geopolitical tensions, resulting in differing perspectives compared to other media outlets. But headlines in the domestic politics and social issues categories showed important differences.

Abortion law or abortion rights?

“We observed a lot of subtle differences in the words they choose when they cover the same high-level topics,” says Hanjia Lyu, a computer science PhD student who was the lead author of the study. “For example, when covering abortion issues, Reason tends to use the term ‘abortion law,’ while CNN underscores its ideological position by using the term ‘abortion rights.’ On a higher level they are both talking about abortion issues, but you can feel the subtle difference in the words that they choose.”

The research team hopes to dig deeper to better understand how and why media outlets use different words to cover the same kind of topics. They say understanding these discrepancies, and when they may indicate media bias, is important for both media outlets and readers alike.

Says Luo: “For consumers, it’s useful to know this information because the echo chamber effect is very strong and people are used to only listening to things they like to hear. Showing the divergence and the increased partisanship may make them aware that they need to be more conscious consumers of news.”

Other coauthors from Luo’s research group include Jinsheng Pan, Weihong Qi, and Zichen Wang. Funding for the study was provided by Rochester’s Goergen Institute for Data Science .

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Articles on Media bias

Displaying 1 - 20 of 63 articles.

US media coverage of new science less likely to mention researchers with African and East Asian names

Hao Peng , Northwestern University

Bias hiding in plain sight: Decades of analyses suggest US media skews anti-Palestinian

Natalie Khazaal , Georgia Institute of Technology

Australian media’s Instagram posts on Gaza war have an anti-Palestine bias. That has real-world consequences

Susan Carland , Monash University

Media mogul Rupert Murdoch resigns − extending Joe Biden’s ongoing good luck streak with the media

Michael J. Socolow , University of Maine

South Africa’s media often portrays foreigners in a bad light. This fuels xenophobia

Sikanyiso Masuku , University of South Africa and Sizo Nkala

News coverage of artificial intelligence reflects business and government hype — not critical voices

Guillaume Dandurand , Institut national de la recherche scientifique (INRS) ; Fenwick McKelvey , Concordia University , and Jonathan Roberge , Institut national de la recherche scientifique (INRS)

Women’s voices are missing in the media – including them could generate billions in income

Ylva Rodny-Gumede , University of Johannesburg

Moving beyond the media’s ‘deficit lens’ is essential for racialised peoples to claim belonging. Here’s how they’re doing it

Sukhmani Khorana , Western Sydney University

Republicans and Democrats see news bias only in stories that clearly favor the other party

Marjorie Hershey , Indiana University

African newspapers can be anti-African too: what my research found

Sisanda Nkoala , Cape Peninsula University of Technology

Serena Williams forced sports journalists to get out of the ‘toy box’ – and cover tennis as more than a game

Erin Whiteside , University of Tennessee

Sex workers in Nigeria deserve fair treatment from the media

Nathan Oguche Emmanuel , National Open University of Nigeria ; Gever Verlumun Celestine , University of Nigeria , and Hashim Muhammad Suleiman , Ahmadu Bello University

Conservatives feel blamed, shamed and ostracized by the media

Doron Taussig , Ursinus College and Anthony M. Nadler , Ursinus College

Male experts outnumber females by 10 to 1 on Ghana media programmes. We found out why

Suzanne Franks , City, University of London

Emily Wilder and journalism’s longstanding Achilles’ heel – partisans who cry bias

Matthew Jordan , Penn State

7 ways to avoid becoming a misinformation superspreader

H. Colleen Sinclair , Mississippi State University

Study sheds light on what it takes for women to succeed – or not – in science in Africa

Allen Muyaama Mukhwana , African Academy of Sciences and Judy Omumbo , African Academy of Sciences

The media have muted the voices of women during COVID-19 : can the tide be turned?

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Bias in media: a case study

Media is a powerful communication tool but can also create and propagate bias. See how bias plays out in the media coverage of the war in Ukraine in 2022.

So much has changed in the past few decades, including our norms for how we speak about social groups.

When university professors in the 1980s asked students and administrators to consider dropping the so-called “generic he” if referring to both male and female students and professors, they were met with resistance. “Of course we mean everybody when we use ‘man’ or ‘he,’” they groused.

Mahzarin remembers an article in her college newspaper criticizing her classroom policy that points would be taken off for use of the “generic he.” But her argument was simple: it is inaccurate to use the pronoun of one group to stand in for another group. She argued that college is a time to learn to write accurately using clear prose.

Today, we look back and smile at the resistance towards dropping the “generic he”; for many English speakers, its usage has since evaporated. 

As this example shows, language is rich and constantly evolving. Today’s language may reveal tomorrow’s bias, often through specific choices of words used to describe situations involving different social groups. Language illuminates bias by reflecting the thoughts and feelings in the mind of the speaker.

For example, do we pick certain words when describing the conversation of a group whose language we don’t understand, like the term “noise” instead of the more neutral “sound”? Language then propagates these habits of thought, as it is shared and because sharing or communication is its primary function. 

In 2022, Russia invaded Ukraine. We got our news from on-the-ground reporters, and some of us noticed bias hiding in their words. A particular reporter, one who was clearly emotionally moved by what he was seeing, said that he was shocked that the people dying in Ukraine were “ blue-eyed and blond-haired .” Another commented, “This isn’t Iraq or Afghanistan…This is a relatively civilized , relatively European city.”

We understand that the pain of war can cause even reporters to be distressed; but in thinking about progress, we would also insist that heartache cannot interfere with fair-minded, unbiased reporting. 

Each of us has made plenty of similar mistakes that reveal our biases through our words. The challenge for all of us, then, is to practice neutrality – to stive to be impartial judges. Yes, it is hard to control how words come out of our mouths, especially when we are stricken with grief or under stress. But those are exactly the moments in which we must test ourselves to see if our behavior is in line with our values. And for that reason to call out possible bias when we hear it, even in our own speech.

Why? Beyond our own desire for fairness, there’s a practical reason: in the words of the Arab and Middle Eastern Journalists Association, biased comments like these can “perpetuate prejudicial responses to political and humanitarian crises.”

So what should we do?

Identify biased speech. Raise awareness wherever you see it. Be kind, because it could very well be you who makes that mistake next! Turn these revelations into moments of teaching and learning. An awareness of bias in language can ultimately lead us all to practice better ways of speaking and hearing.

For instance, when the reporter spoke of the tragedy that had befallen “blue-eyed and blond-haired” people, the newscaster at the desk who was communicating with the reporter on the ground could have followed up with, “And surely we would grieve in the same way for anybody – irrespective of the color of their hair or eyes?” This simple response immediately challenges the idea that a European war is a greater tragedy than others. It brings the bias not only into the awareness of the speaker, but into the awareness of thousands, even millions, of viewers. 

Because no good journalist wishes to communicate that “all people are worthy, but some people are more worthy than others,” a constant alertness to the language used by media and the values it so powerfully propagates is necessary.

Given the vast influence of media or the words of leaders, we must stand ready to analyze and correct biases in language when we see them.

Subscribe to Outsmarting Implicit Bias

Published April 25, 2022

This article was written by Caitlyn Finton and Mahzarin Banaji.

Artwork by Evan Younger.

Support for Outsmarting Implicit Bias comes from Harvard University, PwC, P&G, and Johnson & Johnson.

Media Bias and Democracy in India

By  janani mohan, in south asia.

• June 28, 2021

This article was originally published in South Asian Voices.

As the COVID-19 pandemic rages out of control in India, many are rightly focusing on the content of stories on the death toll and months of lockdown. The lack of journalistic integrity behind some of the stories deepens this grim situation. In April,  reports emerged  that, at the request of the Indian government, Twitter censored 52 tweets criticizing the government’s handling of the pandemic. Meanwhile, pro-government TV channels  blamed  the farmers’ protests for limited oxygen supplies for COVID-19 patients, though supplies were  actually scarce  due to poor public health infrastructure. This reporting is not only misleading and traumatic to those affected by the pandemic, but also poses a major threat to India’s vibrant democracy.

Even before the pandemic, media bias in India existed across the largest newspapers throughout the country, and political forces shape this bias. For example, funds from the government are critical to many newspapers’ operations and budgets, and the current Bhartiya Janata Party (BJP) government has previously  refused to advertise  with newspapers that do not support its initiatives. This pressure leads media to endorse government policies, creating unbalanced reporting where media bias can affect political behavior in favor of the incumbent. Many media outlets enjoy a symbiotic relationship with the government, in turn receiving attention, funding, and prominence. These trends damage India’s democracy and also put journalists critical of the government in danger, threatening their right to physical safety.

Funds from the government are critical to many newspapers’ operations and budgets, and the current Bhartiya Janata Party (BJP) government has previously refused to advertise with newspapers that do not support its initiatives.

Media Bias in India

While the COVID-19 pandemic has exacerbated media bias in India, it is hardly a new phenomenon. A  study  of 30 Indian newspapers and 41 Indian TV channels with the largest viewership rates in the country confirms the existence of rampant media bias during a two-year period from 2017 to 2018. 1

The study relies on rating editorial articles that focus on religious, gender, and caste issues as either liberal, neutral, or conservative; and then compiling these scores by each newspaper to find the overall bias in each outlet. The results unsurprisingly and unfortunately show the consistent existence of media bias—for example, except for eight newspapers, the papers all express biases far from neutral. And this bias consistently correlates with viewers in India expressing similarly biased social, economic, and security attitudes.

What this suggests is either that biases in the media shape viewer attitudes or Indians are viewing outlets that align with their pre-existing views. Meanwhile, political parties capitalize on this bias to influence public attitudes and further their own power. The BJP  spends  almost USD $140 million on publicity per year, with 43 percent of this expenditure focusing specifically on print ads in newspapers. Government advertisements serve as a financial lever for influencing media content and public opinion. For example, during the year leading to the 2019 elections, newspapers that received more advertisement revenue from the BJP were likelier to espouse more conservative ideology and to have more conservative readers.

Bias versus Democracy

This ability of media bias to influence political support in India can contribute significantly to democratic backsliding by harming journalists, preventing freedom of expression and government accountability, and influencing voters. Media bias in itself causes democratic backsliding because the media neither holding the government accountable nor informing the public about policies that strengthen the incumbent’s power can increase authoritarian practices.

In addition, government efforts to constrain the media harms journalists, undemocratically violating citizens’ rights and physical safety. Freedom House  rates  India as only two on a four-point scale for whether there is a “free and independent media,” because of “attacks on press freedom…under the Modi government.” In fact, the government  imprisoned several journalists  in 2020 who reported critically on Prime Minister (PM) Narendra Modi’s response to the pandemic. The crackdown on journalists engendered an unsafe environment for free reporting, a feature of many authoritarian states.

A biased media also prevents citizens from receiving information that might be essential to public wellbeing by filtering information through a lens that supports government interests first. When the BJP cracked down on coverage of COVID-19 last year, journalists were  unable to disseminate  critical information to Indians. This included where migrants suffering from the sudden lockdown could receive necessities—information that could save lives. Notably, these crackdowns also meant an absence of reporting criticizing the government’s response to the pandemic. In a democratic society, a critical press is essential for holding the government accountable for its actions and motivating it to change its practices.  

Media bias plays an influencing role at the voting booth as propaganda can skew voter decisions and perceptions of what is true.

Finally, media bias plays an influencing role at the voting booth as propaganda can skew voter decisions and perceptions of what is true. During India’s 2014 general elections, the BJP advertised more than the Congress Party and voters exposed to more media were  likelier  to vote for the BJP. To influence voters, media bias often utilizes inflammatory messaging to convince more people to vote, selective information to bias what voters believe about the efficacy of the candidates, and appeasement to convince voters that they will personally benefit from voting a certain way. For example, a TimesNow interview of PM Modi before the 2019 elections  made it seem  that Modi’s economic policies—widely criticized as ineffectual—were successful.

From Media Bias to Media Neutrality

Although government measures are exacerbating media bias, the media retains some agency and could work to limit the influence of politics on reporting. Currently, 36 percent of daily newspapers  earn over half  of their total income from the government of India and most major TV stations have owners who served as politicians themselves or who had family members in politics. Although it would be difficult to convince larger outlets to participate since they benefit from their government backing, smaller independent outlets can start this movement towards neutrality. Many small outlets already eschew government funding and report with less biased views. These publications in India therefore deserve more attention and more support to reduce media bias.

While India has some of the  highest circulation  of newspapers in the world, it also unfortunately has high media bias rates and one of the  lowest press freedom rankings  for democracies. This media bias can contribute to democratic backsliding and must be addressed by media outlets. Only then can media in India properly do its job—serving to inform, not influence the public.

The author would like to acknowledge Dr. Pradeep Chhibber, Pranav Gupta, and UC Berkeley for supporting her research measuring media bias in India. All perspectives in this article are her own.

This article was originally published in  South Asian Voices.

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Media Bias Watch: How Stories are Placed

By Henry A. Brechter

AllSides frequently provides case studies that illustrate how bias appears in the media. Read more about AllSides’ 11 Types of Media Bias here.

Tuesday, December 10th was a busy day in the news media, with Democrats announcing two articles of impeachment and President Trump taking major steps toward a new NAFTA, one of his core campaign promises.

But around 2 p.m. EST that day, the “top story” was totally different depending on which side of the news you were reading. This is the result of a common form of bias seen in the news media — bias by story placement (zoom to view specific stories):

The New York Times:

The New York Times placed the impeachment articles in the top spot, with stories on Trump’s reactions to the progressing report on the FBI’s Russia probe and the new trade deal mentioned in secondary positions near the top of the page. The top story spans a majority of space relative to the other news stories on the page.

The Wall Street Journal:

The Wall Street Journal ’s biggest story was news of the trade pact, with a story on Attorney General William Barr’s appearance at the WSJ CEO Council Tuesday morning right next to it, and mention of the impeachment articles directly below it. Here, there is less size difference between the major and secondary stories.

Fox Online News:

Fox News ’ main story was Barr’s reaction to recent developments on the ongoing look at the FBI’s Russia probe. An additional story on that same topic and a story on the impeachment articles were located right under it, with mention of the trade deal on the left sidebar. As with the Times, we see a significant size difference between the top story and secondary stories.

Three of the biggest news sites in the country. Three unique sides of the political spectrum. Three completely different top stories.

The differences are in line with the AllSides Media Bias Ratings for these outlets. The left-leaning source is focusing on stories that are more critical of Republicans and supportive of Democrats; the right-leaning source is doing the exact opposite; and the center-rated source is seemingly giving each side similar exposure.

It’s important to consume news from multiple sources to diversify your perspectives. Instead of subscribing to one point of view, jump around to different sides of the spectrum and choose what the day’s most important stories are for yourself.

Henry A. Brechter is the Daily News Editor at AllSides. He has a Center bias.

Julie Mastrine, Director of Marketing at AllSides, reviewed this piece. She has a Lean Right bias. Samantha Shireman, Information Architect at AllSides, reviewed this piece. She has a Lean Left bias.

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How do we raise media bias awareness effectively? Effects of visualizations to communicate bias

Timo spinde.

1 Department of Computer and Information Science, University of Konstanz, Konstanz, Germany

2 School of Electrical, Information and Media Engineering, University of Wuppertal, Wuppertal, Germany

Christin Jeggle

3 Department of Psychology, University of Konstanz, Konstanz, Germany

Magdalena Haupt

Wolfgang gaissmaier, helge giese, associated data.

Data are available at https://osf.io/e95dh/ .

Media bias has a substantial impact on individual and collective perception of news. Effective communication that may counteract its potential negative effects still needs to be developed. In this article, we analyze how to facilitate the detection of media bias with visual and textual aids in the form of (a) a forewarning message, (b) text annotations, and (c) political classifiers. In an online experiment, we randomized 985 participants to receive a biased liberal or conservative news article in any combination of the three aids. Meanwhile, their subjective perception of media bias in this article, attitude change, and political ideology were assessed. Both the forewarning message and the annotations increased media bias awareness, whereas the political classification showed no effect. Incongruence between an articles’ political position and individual political orientation also increased media bias awareness. Visual aids did not mitigate this effect. Likewise, attitudes remained unaltered.

Introduction

The Internet age has a significant impact on today’s news communication: It allows individuals to access news and information from an ever-increasing variety of sources, at any time, on any subject. Regardless of journalistic standards, media outlets with a wide reach have the power to affect public opinion and shape collective decision-making processes [ 1 ]. However, it is well known that the wording and selection of news in media coverage often are biased and provide limited viewpoints [ 2 ], commonly referred to as media bias . According to Domke and colleagues [ 3 ], media bias is a structural, often wilful defect in news coverage that potentially influences public opinion. Labeling named entities with terms that are ambiguous in the concepts they allude to (e.g. "illegal immigrants" and "illegal aliens" [ 4 ] or combining concepts beyond their initial contexts into figurative speech that carry a positive or negative association ("a wave of immigrants flooded the country") can induce bias. Still, the conceptualization of media bias is complex since biased and balanced reporting cannot be distinguished incisively [ 5 ]. Many definitions exist, and media bias, in general, has been researched from various angles, such as psychology [ 6 ], computer science [ 7 ], linguistics [ 8 ], economics [ 9 ], or political science [ 10 ]. Therefore, we believe advancement in media bias communication is relevant for multiple scientific areas.

Previous research shows the effects of media bias on individual and public perception of news events [ 6 ]. Since the media are citizens’ primary source of political information [ 11 ], associated bias may affect the political beliefs of the audience, party preferences [ 12 ] and even alter voting behavior [ 13 ]. Moreover, exposure to biased information can lead to negative societal outcomes, including group polarization, intolerance of dissent, and political segregation [ 14 ]. It can also affect collective decision-making [ 15 ]. The implications of selective exposure theory intensify the severity of biased news coverage: Researchers observed long ago that people prefer to consume information that fits their worldview and avoid information that challenges these beliefs [ 16 ]. By selecting only confirmatory information, one’s own opinion is reaffirmed, and there is no need to re-evaluate existing stances [ 17 ]. In this way, the unpleasant feeling of cognitive dissonance is avoided [ 18 ]. Isolation in one’s own filter bubble or echo chamber confirms internal biases and might lead to a general decrease in the diversity of news consumption [ 14 ]. This decrease is further exacerbated by recent technological developments like personalized overview features of, e.g., news aggregators [ 19 ]. How partisans select and perceive political news is thus an important question in political communication research [ 20 ]. Therefore, this study tries to test ways to increase the awareness of media bias (which might mitigate its negative impact) and the partisan evaluation of the media through transparent bias communication.

Media bias communication

Media bias occurs in various forms, for example, whether or how a topic is reported (D’Alessio & Allen, 2000) and may not always be easy to identify. As a result, news consumers often engage with distorted media but are not aware of it and exhibit a lack of media bias awareness [ 21 ]. To address this issue, revealing the existence and nature of media can be an essential route to attain media bias awareness and promote informed and reflective news consumption [ 19 ]. For instance, visualizations may generally help to raise media bias awareness and lead to a more balanced news intake by warning people of potential biases [ 22 ], highlighting individual instances of bias [ 19 ], or facilitating the comparison of contents [ 2 , 23 ].

Although knowledge of how to communicate media bias effectively is crucial, visualizations and enhanced perception of media bias have only played a minor role in existing research, and several approaches have not yet been investigated. Therefore, this paper tests how effectively different strategies promote media bias awareness and thereby may also help understand common barriers to informed media consumption. We selected three major methods in related work [ 19 , 22 ] on the topic to further investigate them in one combined study: forewarning messages, text annotations, and political classifications. Theoretical foundations of bias messages and visualizations are yet scarce, and neither in visualization theory nor in bias theory, suitable strategies in the domain have been extensively tested.

Forewarning message

According to socio-psychological inoculation theory [ 24 ], it is possible to pre-emptively confer psychological resistance against persuasion attempts by exposing people to a message of warning character. It is similar to the process of immunizing against a virus by administering a weakened dose of the virus: A so-called inoculation message is expected to protect people from a persuasive attack by exposing them to weakened forms of the persuasion attempt. Due to the perceived threat of the forewarning inoculation message, people tend to strengthen their own position and are thus more resistant to influences of imminent persuasion attacks [ 25 ]. Therefore, one strategy to help people detect bias is to prepare them ahead of media consumption that media bias may occur, thereby "forewarning" them against biased language influences. Such warnings have been widely established in persuasion and shown to be effective in different applied contexts [ 26 ]. Furthermore, such warnings also seem to help not only to protect attitudes against influences but also to determine the quality of a piece of information [ 27 – 29 ] and communicate the information accordingly [ 30 ]. For biased language, this may work specifically by focusing the reader’s attention on a universal motive to evaluate the accuracy of information while relying on the individual’s capacity to detect the bias when encountered [ 30 ]; Bolsen & Druckman, 2015).

Annotations

Other than informing people in advance about bias occurrence, a further approach is to inform them during reading, thereby increasing their awareness of biased language and providing direct help to detect it in an article. Recently, there has been a lot of research on media bias from information science, but it is mainly concerned with its identification and detection [ 31 – 34 ]. However, whereas some research concerning the effects of visualizations of media bias in news articles to detect bias are promising (here: flagging fake news as debunked [ 35 ]) others did not find such effects, potentially also due to the technical issues in accurately annotating single articles [ 19 ]. Still, they offer a good prospect to enable higher media bias awareness and more balanced news consumption. We show our annotation visualization in Fig 1 .

Example of the bias annotation "subjective term". Boxed annotation appeared by moving the cursor/finger over the highlighted text section.

Political classification

Another attempt to raise media bias awareness is a political classification of biased material after readers have dealt with it. An and colleagues [ 36 ] proposed an ideological left-right map where media sources are politically classified. The authors suggest that showing a source’s political leaning helps readers question their attitudes and even promotes browsing for news articles with multiple viewpoints. Likewise, several other studies indicate that feedback on the political orientation of an article or a source may lead to more media bias awareness and a more balanced news consumption [ 19 ]. Additionally, exposing users to multiple diverse viewpoints on controversial topics encourages the development of more balanced viewpoints [ 23 ]. A study of Munson and colleagues (2013) further suggests that a feedback element indicating whether the user’s browsing history consists of biased news consumption modestly leads to a more balanced news consumption. Based on these findings, we will test whether the sole representation of a source’s leaning helps raise bias awareness among users on the condition that the article is classified as politically skewed. We show our political classification bar in Fig 2 .

Example of an article classification as being politically left-oriented.

Partisan media bias awareness

Attempts to raise media bias awareness may be further complicated by the fact that the detection of media bias and the evaluation of news seem dependent on the political ideology of the beholder [ 37 – 41 ]. However, this partisan effect is not only apparent in neutral reporting: It is supposed that individuals perceive biased content that corresponds to their opinion as less biased [ 38 ] and biased content that contradicts their viewpoints as more biased [ 41 ].

These findings suggest that incongruence between the reader’s position and the news article’s position may increase media bias perception of the article, whereas congruence may decrease it. Thus, partisan media consumers may engage in motivated reasoning to overcome cognitive dissonance experienced when encountering media bias in any news article generally in line with their viewpoints [ 42 ]. According to Festinger [ 18 ], cognitive dissonance is generated when a person has two cognitive elements that are inconsistent with each other. This inconsistency is assumed to produce a feeling of mental discomfort. People who experience dissonance are motivated to reduce the inconsistency because they want to avoid or reduce this negative emotion.

Furthermore, Festinger notes that exposure to messages inconsistent with one’s beliefs could create cognitive dissonance, leading people to avoid or reduce negative emotions. In line with this notion, raising media bias awareness could increase experienced cognitive dissonance and thereby lead to even more partisan ratings of bias. Another explanation of the phenomenon of partisan bias ratings is varying norms about what content is considered appropriate in media coverage dependent on one’s political identity[ 43 ]. Other researchers focus on the inattention to the quality of news and the motive to only support truthful news [ 44 ]. Both approaches lead us to expect opposite results for the partisanship of the media bias ratings with increased media bias awareness as created by our proposed visualizations: Partisanship of ratings should decrease rather than increase as people are reminded of more general norms and accuracy motives [ 27 ].

Study aims and hypotheses

This project aims to contribute to a deeper understanding of effective media bias communication. To this end, we create a set of bias visualizations revealing bias in different ways and test their effectiveness to raise awareness in an online experiment. Following the respective literature elaborated above for each technique, we would expect enhanced media bias awareness by all visualizations:

• H1a: A forewarning message prior to news articles increases media bias awareness in presented articles.
• H1b: Annotations in news articles increase media bias awareness in presented articles.
• H1c: A political classification of news articles increases media bias awareness in presented articles.

Another goal of this study is to understand better the reader’s political orientation in media bias awareness. In line with the findings of partisan media bias perception (hostile media effect; Vallone et al., 1985), we adopt the following hypothesis:

• H2: Presented material will be rated less biased if consistent with individual political orientation.

Furthermore, we assume, following the attentional and normative explanation of partisanship in ratings rather than cognitive dissonance theory, the following effect:

• H3: Bias visualizations will mitigate the effects of partisan bias ratings.

Participants

A total of 1002 participants from the US were recruited online via Prolific in August of 2020. A final sample of N = 985 was included in the analysis (51% female; age : M = 32.67; SD = 11.95 ) . The excluded participants did not fully complete the study or indicated that their data might not be trusted in a seriousness check. The target sample size was determined using power analysis, so that small effects ( f = 0.10) could be found with a power of .80 [ 45 ]. The online study was scheduled to last approximately 10 minutes, for which the participants received £1.10 as payment.

Design and procedure

The experiment was conducted online in Qualtrics ( https://www.qualtrics.com ). It operated with fully informed consent, adheres to the Declaration of Helsinki, and was conducted in compliance with relevant laws and institutional guidelines, including the ones of the University of Konstanz ethics board. All participants confirmed their consent in written form and were informed in detail about the study, the aim, data processing, anonymization, and other background information.

After collecting informed consent and demographic information, we conducted an initial attitude assessment which asked for their general perception of the presented topic on three dimensions and personal relevance. Next, participants read one randomly selected biased news article (either liberal or conservative), randomly supplemented by any combination of the visual aids (forewarning message, annotations, political classification). Thus, the study had a 2x2x2 forewarning message (yes/no) x annotations (yes/no) x political map (yes/no) between design. The article also varied between participants in both article position (liberal/conservative) and article topic (gun law/abortion) to determine the results’ partialness and generalizability. Finally, attitudes towards the topic were reassessed, followed by a seriousness check.

Study material

Visual aids.

Forewarning message . The forewarning message consisted of a short warning and was displayed directly before the news article. It reads: " Beware of biased news coverage . Read consciously . Don’t be fooled . The term ’media bias’ refers to , in part , non-neutral tonality and word choice in the news . Media Bias can consciously and unconsciously result in a narrow and one-sided point of view . How a topic or issue is covered in the news can decisively impact public debates and affect our collective decision making ." Besides, an example of one-sided language was shown, and readers were encouraged to consume news consciously.

Annotations . Annotations were directly integrated into the news texts. Biased words or sentences were highlighted [ 46 ], and by hovering over the marked sections, a short explanation of the respective type of bias appeared. For example, if moving the cursor over a very one-sided term, the following annotation would be displayed: " Subjective term : Language that is skewed by feeling , opinion or taste ." Annotations were based on ratings of six members of our research group, where phrases had to be nominated by at least three raters. The final annotations can be found in the supplementary preregistration repository accompanying this article at https://osf.io/e95dh/‌?view_only=d2fb5dc‌2d64741e393b30b9ee6cc7dc1 (Non-anonymous Link is made accessible in case of acceptance). We followed the guidelines applied in existing research to teach annotators about bias and reach higher-quality annotations [ 47 ]. In future work, we will further increase the number of raters, as we address in the discussion.

Political classification . A political classification in the form of a spectrum from left to right indicated the source’s political ideology. It was displayed immediately after the presented article and based on the rating of the webpage Allsides.

We used four biased news articles that varied in topic and political position. Each participant was assigned to one article. The two topics covered were gun law and the debate on abortion, with either a liberal or conservative article position. Topics were selected because we considered them controversial issues in the United States that most people are presumably familiar with. To ensure that articles were biased, they were taken from sources deemed extreme according to the Allsides classification. Conservative texts were taken from Breitbart.com ; liberal articles were from Huffpost.com and Washingtonpost.com . We also conducted a manipulation check to determine whether participants perceived political article positions in line with our assumptions: Just after reading the article, participants were asked to classify its political stance on a visual analogue scale (-5 = very liberal to 5 = very conservative ). To ensure comparability, articles were shortened to approximately the same length, and respective sources were not indicated. All article texts used are listed together with their annotations in the supplementary preregistration repository accompanying this article (we show the link on the previous page).

Media bias awareness

Five semantic differentials assessed media bias awareness on fairness, partialness, acceptableness, trustworthiness, and persuasiveness [ 48 – 50 ] on visual analogue scales (" I think the presented news article was… "). Media bias awareness was established by averaging the five items and recoded to range from -5 = low bias awareness to 5 = high bias awareness ( α = .88).

Political orientation

The variable political orientation was measured on a visual analogue scale ranging from –5 = very conservative to 5 = very liberal ), introduced with the question " Do you consider yourself to be liberal , conservative , or somewhere in between ?" adopted by Spinde and colleagues [ 19 , 51 ]. Likewise, we assessed the perceived stance of the read article on the same scale introduced with the question " I think the presented news article was… ".

Attitudes towards article topic

Attitudes were assessed before and after the article presentation by a three-item semantic differential scale ( wrong - right , unacceptable - acceptable , bad - good ) evaluating the two topics (" Generally , laws restricting abortion/ the use of guns are . . ."; α = .99). The three items were averaged per topic to yield a score from (–5 = very conservative attitude to 5 = very liberal attitude). Besides, we assessed topic involvement by one item before the article presentation (" To me personally , laws restricting the use of guns/ abortions are… irrelevant-relevant") on a scale from –5 to 5.

Statistical analysis

To test effects of the visual aids on media bias perception, we used ANOVAs with effect coded factors in a forewarning message (yes/no) x annotations (yes/no) x political map (yes/no) x2 article position (liberal/conservative) x2 article topic (gun law/abortion) between design. For analyses testing political ideology effects, this was generalized to a GLM with standardized political orientation as an additional interacting variable followed by a simple effects analysis. The same model was applied to the second attitude rating, with first attitude rating and topic involvement as covariates for attitude change. This project and the analyses were preregistered with the DOI https://osf.io/e95dh/?view_only=d2fb5dc2d64741e39‌3b30b9ee6cc7dc1 (Non-anonymous Link is made accessible in case of acceptance). All study materials, code, and data are available there.

Manipulation check and other effects on perceived political stance of the article

Overall, the positions of the political articles were perceived as designed ( article position : F (1, 953) = 528.67, p < .001, η p 2 = .357): Articles assigned a liberal position were perceived more liberal ( M = 1.60, SD = 2.70), whereas conservative articles were rated more conservative ( M = –1.98, SD = 2.26). This difference between the conservative and the liberal article was more pronounced, when a forewarning message ( F (1, 953) = 7.33, p = .007, η p 2 = .008), annotations ( F (1, 953) = 3.96, p = .047, η p 2 = .004), or the political classifications were present ( F (1, 953) = 9.12, p = .003, η p 2 = .009; see Fig 3 ). The combination of forewarning and classification further increased the difference ( F (1, 953) = 5.28, p = .022, η p 2 = .006).

Across all conditions, liberal articles were perceived to be more liberal and conservative articles more conservative. The interventions increased the differences between the two ratings. Dots represent means, and lines are standard deviations.

Effects of visual aids on media bias perceptions

Testing the effects of the visual aids on media bias perceptions in general, we found that both the forewarning message ( F (1, 953) = 8.29, p = .004, η p 2 = .009) and the annotations ( F (1, 953) = 24.00, p < .001, η p 2 = .025) increased perceived bias, which we show in Fig 4 . However, we found no effect of the political classification ( F (1, 953) = 2.56, p = .110, η p 2 = .003) and no systematic higher-order interaction involving any of the manipulations ( p ≥ .085, η p 2 ≤ .003). Moreover, there were differences in media bias perceptions of the specific articles ( topic x article position : F (1,953) = 24.44, p < .001, η p 2 = .025). The two found main effects were by and large robust when testing it per item of the media bias perception scale (forewarning had no significant effect on partialness and persuasiveness) or in a MANOVA ( forewarning : F (5, 949) = 5.22, p < .001, η p 2 = .027; annotation : F (5, 949) = 6.25, p < .001, η p 2 = .032).

The forewarning message, as well as annotations, increased media bias awareness. Dots represent means, and lines are standard deviations.

Partisan media bias ratings

When considering self-indicated political orientation and its fit to the article position , we found that media bias was perceived less for articles consistent with the reader’s political orientation ( F (1,921) = 113.37, p < .001, η p 2 = .110): For conservative articles, liberal readers rated conservative articles more biased than conservative readers (β = 0.32; p < .001; 95%CI[0.25; 0.38]). Conversely, liberal articles were rated less biased by liberals (β = –0.20; p < .001; 95%CI[–0.27; –0.13]), indicating a partisan bias rating for both political isles, which we show in Fig 5 .

Bias awareness increases when the article is not aligned with the persons’ political position. Shades show 95% confidence intervals of the regression estimation.

This partisan rating of articles was unaffected by forewarning ( F (1,921) = 1.52, p = .218, η p 2 = .002), annotations ( F (1,921) = 0.26, p = .612, η p 2 < .001), and political classification ( F (1,921) = 2.72, p = .010, η p 2 = .003). Yet, with increasing liberalness of the reader, the combination of forewarning and annotation was slightly less effective on the detection of bias ( F (1,921) = 4.19, p = .041, η p 2 = .005). Furthermore, there were some topic-related differences irrelevant to the current hypotheses (higher bias was perceived for the gun laws articles ( topic : F (1,921) = 11.32, p < .001, η p 2 = .012) and specifically so for the liberal one ( topic x article position : F (1,921) = 23.86, p < .001, η p 2 = .025) with some uninterpretable minor higher order interaction ( forewarning x annotation x classification x political orientation x topic : F (1,921) = 4.10, p = .043, η p 2 = .004)).

Effects on attitudes

By and large, attitudes on the topics were not affected by the experiment: While attitudes after reading the article were in line with prior attitudes ( F (1,919) = 2415.42, p < .001, η p 2 = .724) and individual political orientation ( F (1,919) = 34.54, p < .001, η p 2 = .036), neither article position ( F (1,919) = 2.63, p = .105, η p 2 = .003) nor any of the visual aids had any general impact ( p ≥ .084, η p 2 ≤ .003). Likewise, neither of the aids interacted with the factor article position ( p ≥ .298, η p 2 ≤ .001). Solely, there were some additional minor topic-specific significant effects of the annotation combined with the forewarning ( F (1,919) = 4.77, p = .0292, η p 2 = .005) and an increased liberalness of attitude with higher topic involvement ( F (1,919) = 4.31, p = .038, η p 2 = .005), that we want to disclose, but deem irrelevant to our hypotheses and research questions.

In this study, we tested different techniques to communicate media bias. Our experiment revealed that presenting a forewarning message and text annotations enhanced awareness of biased reporting, while a political classification did not. All three methods (forewarning, annotation, political classification) impacted the political ideology rating of the presented article. Furthermore, we found evidence for partisan bias ratings: Participants rated articles that agreed with their general orientation to be less biased than articles from the other side of the political spectrum. The positive effect of the forewarning message on media bias ratings, albeit small, is in line with a few other findings of successful appeals to and reminders of accuracy motives [ 30 ]. In addition, it accords with the notion that reflecting on media bias involves some efforts [ 44 , 52 ], so motivating people to engage in this process can help detect bias.

Regarding the effects of in-text annotations, our finding differs from a previous study of a similar design [ 19 ], which did not identify the effect due to a lack of power and less optimal annotations. While news consumers may generally identify outright false or fake [ 53 ] news, detecting subtle biases can profit from such aids. This indicates that bias detection is far from ideal, particularly in more ambiguous cases. As in-text annotation and forewarning message effects were independent of each other, participants seemingly do not profit from the combination of aids.

On the other hand, the political classification could solely improve the detection of the political alignment of the text (which was also achieved by both other methods) but not help detecting biased language. Subsequently, the detection of biased language and media bias itself does not appear to be directly related to an article’s political affiliation.

Our study also replicates findings that the detection of media bias and fake news is affected by individual convictions [ 30 , 40 , 42 ]: We found that participants could detect media bias more readily if there was an incongruence between the participant’s and the article’s political ideology. Such a connection may be particularly true for detecting more subtle media biases and holding an article in high regard compared to successfully identifying outright fake news, for which a reversed effect could be found in some instances (Pennycook & Rand, 2019).

In addition, interventions were ineffective to lower such partisan effects. Similarly, attitudes remained relatively stable and were not affected by any of the visual aids. Making biased language more visible and reminding people of potential biases could apparently not help them overcome their ideology in rating the acceptance of an article when there is no clear indication that the information presented in the article is fake but solely biased. Likewise, the forewarning message successfully altered the motivation to look for biased language, but did not decrease the effects of political identity on the rating: While being able to detect the political affiliation of an article, it seems that participants were not capable of separating the stance of the article from its biased use of language, even when prompted to do so. In the same vein, effects were not more pronounced when the political classification was further visualized, potentially pointing to the notion that the stance is also detected without help (after all, while the manipulations increased the distinction between liberal and conservative articles, the article’s position was reliably identified even without any supporting material) and that partisan ratings are not a deliberate derogatory act. Furthermore, the problem of partisan bias ratings also did not increase with increased media bias awareness via the manipulations, as could have been expected by cognitive dissonance theory.

For future work, we will improve the representativeness of the surveyed sample, which limits far-reaching generalizations at this point. Additionally, we will increase the generalizability by employing articles that are politically neutral or exhibit comparatively low bias. Both forewarning and annotations may have increased ratings in this study, but it is unclear whether they also aid in identifying low-bias articles and leading to lower ratings, respectively. Improving the quality of our annotations by including more annotators is an additional step towards exhausting potential findings. We will also investigate how combinations of the visualizations and strategies work together and conduct expert interviews to determine which applications would be of interest in an applied scenario. Still, the current study shows that two of our interventions raised attention to biased language in media, giving a first insight into the yet sparsely tested field of presenting media bias to news consumers.

Furthermore, there is a great challenge in translating these experimental interventions to applications used by news consumers in the field. While forewarning messages could be implemented quite simply in the context of other media, for instance, as a disclaimer (see [ 30 ]), we hope that automated classifiers on the sentence level will prove to be an effective tool to create instant annotating aids for example as browser add-ons. Even though recent studies show promising accuracy improvements for such classifiers [ 31 , 32 ], we still want to note that much research needs to be devoted to finding stable and reliable markers of biased language. Future work also has great potential to consider these strategies as teaching tools to train users in identifying bias without visual aids. This could offer a framework for a large-scale study in which additional variables measuring previous news consumption habits could be employed.

In the context of our digitalized world, where news and information of differing quality are available everywhere, our results provide important insights for media bias research. In the present study, we were able to show that forewarning messages and annotations increased media bias awareness among readers in selected news articles. Also, we could replicate the well-known hostile media bias that consists of people being more aware of bias in articles from the opposing side of the political spectrum. However, our experiment revealed that the visualizations could not reduce this effect, but partisan ratings rather seemed unaffected. In sum, digital tools uncovering and visualizing media bias may help mitigate the negative effects of media bias in the future.

Funding Statement

This work was supported by the German Research Foundation [DFG] ( https://www.dfg.de/ ) under Grant 441541975, the German Research Foundation Centre ( https://www.dfg.de/ ) of Excellence 2117 "Centre for the Advanced Study of Collective Behaviour" (ID: 422037984). It was also supported by the Hanns-Seidel Foundation ( https://www.hss.de/ ) and the German Academic Exchange Service (DAAD) ( https://www.daad.de/de/ ). None of the funder played any role in the study design or any publication related decisions.

Data Availability

Harvard Study reveals media bias

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Example of Media Bias:

A new report from Harvard Kennedy School’s Shorenstein Center on Media, Politics and Public Policy analyzed news coverage of President Trump’s first 100 days in office.

The report is based on an analysis of:

• news reports in the print editions of The New York Times, The Wall Street Journal, and The Washington Post
• the main newscasts of CBS, CNN, Fox News, and NBC
• and three European news outlets – the UK’s Financial Times and BBC, and Germany’s ARD

To arrive at the percentages, Harvard obtained data from Media Tenor, which “codes” media according to its topic, source, and tone. The broadcast portion of the study accounts for reporting on CNN’s The Situation Room, CBS Evening News, Fox’s Special Report, and NBC Nightly News, but does not represent talk show coverage. Newspaper analysis incorporates all sections but obituaries, sports, and letters to the editor.

Trump’s coverage during his first 100 days set a new standard for negativity. Findings include:

• President Trump has received unsparing coverage for most weeks of his presidency, without a single major topic where coverage, on balance, was more positive than negative, setting a new standard for unfavorable press coverage of a president.
• Every news outlet in the study was negative more often than positive.
• Fox was the only news outlet that came close to giving Trump positive coverage overall – though they did not – 52% of news reports on Fox were negative towards President Trump. Only 48% were positive. (There was variation in the tone of Fox’s coverage depending on the topic. Trade and terrorism were news categories where Fox’s coverage was starkly different from that of the other outlets. Whereas their coverage in these areas tipped strongly in the negative direction, Fox’s coverage tipped strongly positive. This just shows that those are two areas where Fox executives agree with Trump’s position. Thus the positive coverage.)

The study also divided news items across topics. On immigration, healthcare, and Russia, more than 85% of reports were negative. (Consider the fact that on illegal immigration, President Trump has broad public support in his effort to crack down on sanctuary cities.)

On the economy, news reports were still negative, but less negative than other topics – 54% negative to 46% positive.  It should be difficult to publish negative stories about the economy when it is improving.

The study highlighted one exception: Trump got overwhelmingly positive coverage for launching a cruise missile attack on Syria.

Around 80% of all reports were positive about that.

A third major part of Harvard’s study compared coverage of President Trump to that of the three previous presidents – Barack Obama, George W. Bush, and Bill Clintons’ first 100 days.

Trump received 80 percent negative coverage, with only 20 percent of news reports were positive. Compare that to:

• President Obama, who received 41 percent negative coverage and 59 percent positive coverage.
• President Bush’s news coverage was 57 percent negative – 43 percent positive
• President Clinton, 60 percent negative, 40 percent positive

“The media needs different narrative frames [when reporting on President Trump] – not just an antagonistic one,” said Nicco Mele, director of Harvard’s Shorenstein Center, to The Daily Caller News Foundation.

(Compiled from reports at: Harvard’s shorensteincenter.org , dailycaller.com and heatst.com .)

To accurately identify different types of bias, you should be aware of the issues of the day, and the liberal and conservative perspectives on each issue.

• Selection of Sources
• Story Selection

1. How do you think persistent and widespread negative coverage of President Trump affects the way people view him and his administration?

2. What to you do to avoid being influenced / having your views shaped by the media?

CHALLENGE: Check out the editorial cartoons found at several of the media sources evaluated in the Harvard study. What percentage of the cartoons about President Trump are positive? negative? neutral?

Scroll down to the bottom of the page for the answers.

1. Opinion question. Answers vary.

Twitter locks out journalist over tweet criticizing marxist BLM founder

April 15, 2021.

NBC’s Today show baffled by lower COVID rates in reopened states

April 14, 2021.

AP dismisses attorney’s claims of election tampering

November 18, 2020.

Media Networks Cut Off Press Conferences

November 11, 2020.

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The Media Did Not Make Up Trump’s Russia Scandal

Liberal bias mostly exists outside politics coverage..

NPR reporter Uri Berliner wrote an essay for The Free Press arguing that the network has lost chunks of its audience by growing too dogmatically progressive. Some of the evidence supports his claim. Unfortunately, he undermines his case by leading with an example that in no way vindicates the thesis, and actually undermines it: coverage of the Trump-Russia scandal .

Berliner presents the story as a nothingburger that NPR breathlessly hyped and then ignored when it turned out to exonerate the president:

“Persistent rumors that the Trump campaign colluded with Russia over the election became the catnip that drove reporting. At NPR, we hitched our wagon to Trump’s most visible antagonist, Representative Adam Schiff.  Schiff, who was the top Democrat on the House Intelligence Committee, became NPR’s guiding hand, its ever-present muse. By my count, NPR hosts interviewed Schiff 25 times about Trump and Russia. During many of those conversations, Schiff alluded to purported evidence of collusion. The Schiff talking points became the drumbeat of NPR news reports. But when the Mueller report found no credible evidence of collusion, NPR’s coverage was notably sparse. Russiagate quietly faded from our programming.”

Even though Republicans have repeated this ad nauseam to the point where The Free Press would blithely state it as fact, it is simply not true that the Mueller report “found no credible evidence of collusion.”

First, establishing “collusion” was explicitly not the objective of the Mueller investigation. Mueller saw his job as identifying criminal behavior. Collusion is not a crime. The Mueller report stated clearly that it was not attempting to prove whether or not Trump colluded with Russia:

In evaluating whether evidence about collective action of multiple individuals constituted a crime, we applied the framework of conspiracy law, not the concept of “collusion.” In so doing, the Office recognized that the word “collud[e]” was used in communications with the Acting Attorney General confirming certain aspects of the investigation’s scope and that the term has frequently been invoked in public reporting about the investigation. But collusion is not a specific offense or theory of liability found in the United States Code, nor is it a term of art in federal criminal law. For those reasons, the Office’s focus in analyzing questions of joint criminal liability was on conspiracy as defined in federal law.

Nonetheless, Mueller found extensive evidence of collusion between the Trump campaign and Russia. The evidence was summarized in a report by Just Security . It uncovered multiple secret meetings and communications between the two, including, but not limited to. Trump campaign officials met with Russian agents in Trump Tower and were receptive to the offer of campaign assistance; Russian agents shared with Trump their plan to leak embarrassing emails; Trump’s campaign manager shared polling data with a figure linked to Russian intelligence; Trump appeared to have advance knowledge of the timing of the release of stolen Russian emails; and the campaign and Russia coordinated a response to Obama administration sanctions punishing Russia for its efforts on Trump’s behalf.

But because collusion is not a crime, Mueller refrained from stating an opinion as to whether this extensive pattern of furtive meetings in pursuit of a shared objective constituted “collusion.”

There was an investigation into whether Trump’s campaign colluded with Russia. That investigation was conducted by the bipartisan  Senate Intelligence Committee . And that report found even more evidence of collusion, including multiple links between Russian intelligence and the Russian figures interfacing with Trump’s campaign. The Senate identified Konstantin Kilimnik, the business partner of Trump campaign manager Paul Manafort, as a Russian intelligence agent. And it found two pieces of evidence that “raise the possibility of Manafort’s potential connection to the hack-and-leak operations” — the most direct kind of collusion — that it redacted for national-security reasons.

The Senate Intelligence report came out more than a year after the Mueller report and received a fraction of the media attention devoted to Mueller. But that disparity is not, as Berliner frames it, evidence of anti-Trump bias. It’s evidence of the opposite. The news media allowed Trump’s “no collusion” to misleadingly frame Mueller’s investigation and then buried the report that did investigate collusion.

In my experience, if you tell a conservative that there’s a damning story about a Republican the mainstream media ignored, they’ll look at you like you said there are live aliens in a government building. They’re not wrong that the mainstream media has a great deal of liberal bias.

In my view, though, that bias exerts the strongest impact on cultural coverage and on siloed social liberal beats, especially ones related to identity politics, that often simply treat progressive activists as authority figures and convey their perspective uncritically. The New York Times became the target of left-wing protests because it covered the youth gender-medicine story with traditional journalistic methods rather than simply regurgitating activist talking points, as many other publications have done. The Times continues to stand out from other American media institutions in its idiosyncratic decision to cover divisions within the youth gender medical field. The Times wrote about a major new U.K. report finding casting doubt on medicalization of gender-questioning youth, but most American news outlets have covered the story in the same way Fox News covers stories that embarrass Republicans: not at all.

Yet that bias on social liberalism and culture does not equate to coverage of hard political news, which still retains the traditional features of reporting the claims of both parties. Both the mainstream media and its critics would benefit from thinking more carefully about the very different ways parts of their organizations have treated norms of objectivity.

Berliner thinks the Russia story is evidence the news media is hopelessly biased to the left. If anything, his misunderstanding of the story shows the bias is not as bad as he thinks.

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Hilary Cass’s proposals are mostly common sense. She must reject anti-trans bias with the same clarity

By failing to take on clinicians who doubt the very existence of trans children and young people, the review lets down those it seeks to support

T he long-awaited Cass review of gender identity services (Gids) for children and young people is finally here, and people with a wide variety of views appear to be welcoming it. In more civil, fact-based times, in which transness was accepted as just another example of human variation, this outcome could be to its credit: appropriate for a review of clinical services by an expert clinician.

However, we do not live in such times. Instead, in recent years, the UK has fallen to 15th in European LGBT+ equality rankings (in 2016, the UK ranked third ) and was highlighted by the Council of Europe alongside Hungary, Turkey and Russia as a state where LGBT+ rights are under attack from political figures, including governments. We are also experiencing a steep rise in transgender hate crimes, which a UN report directly attributes to “the toxic nature of the public debate surrounding sexual orientation and gender identity”.

This context is important for understanding the Cass review’s rather confounding reception. Based on the coverage in the UK’s rightwing media, where equality for trans people is most loudly and regularly opposed, you might have been convinced that Hilary Cass agrees with them and them alone (The Daily Mail’s front page hailed her as “a voice of sanity”; the Times claimed the report “rejected” the use of puberty blockers outright). However, this is not the case.

Cass criticises Gids’ long waiting lists. My involuntary immersion in this topic for almost a decade enables me to report that the trans community fervently agrees with this, and has been sounding the alarm for years. Cass criticises the lack of broader mental health care provision, including treatment for eating disorders. The community agrees, as would anyone who knows the first thing about NHS mental health provision. Cass cites the lack of autism awareness and assessment. Again, the community – well aware and unafraid of our propensity for neurodivergence – agrees. Cass calls for more and more local Gids service provision. Unsurprisingly, the trans community agrees. Cass bemoans the lack of a peer-reviewed evidence base for trans healthcare. Right there with you, Doc (although there is plenty of research you decided to exclude).

I could go on, but you get the idea. The key words – read plainly and in good faith – can hardly be disagreed with either. Care for trans young people that is “unhurried, holistic, therapeutic, safe and effective”? What’s to dislike? This is only what prospective patients, patients and their parents and caregivers have been calling for all along.

In reality, the problem has never been disagreement about how to care for trans children and young people. Rather, individuals genuinely motivated to create such services have been effectively sidelined by an overwhelmingly more powerful coalition of politicians, journalists and, indeed, healthcare workers who are motivated by an anti-trans ideology – a need to assert and somehow “prove”, to exclusion of all other possibilities, that trans people like me do not, in fact, exist. And, therefore, that we do not spend the first 18 years of ours lives as children. What many trans adults like me fear is that Cass has fallen into the trap of reflecting and therefore given credence to anti-trans bias.

Take puberty blockers, for example. Young people hoping to be prescribed this previously-uncontroversial puberty delayer, including those I’ve been directly in contact with, usually have to wait so long for appointments that they age out of Gids before the conversation even starts. In 2022, 378 children and young people were eligible to be prescribed blockers on the NHS, a relatively small number by any measure. Likewise with masculinising or feminising hormones for under-18s. The review makes this sound like previously common practice. In reality, such a step would only be considered for someone aged 16-18 and is even rarer.

There are more insidious examples too. Cass makes reference to clinicians feeling unable to raise concerns about the slow and inadequate services being provided by Gids. Justin Webb on the Today programme asked whether this by all accounts legitimate criticism was stifled because clinicians feared being accused of “transphobia”. Cass goes some way to agreeing, but then focuses on conciliation, saying, that “whatever the reason” for clinicians’ concerns, she believes everyone was sincerely trying to do their best for their patients.

That failure to add context reflects a lack of context in the report itself: in which a picture is painted of clinicians who all want the best for their young patients, and have been let down by a lack of evidence. That is not a complete picture. Take Dr David Bell, the psychiatrist behind a critical report of the Tavistock centre , and who has welcomed the Cass review. Bell is often presented as a moderate critic of Gids and yet has argued that trans children do not exist in nature but have been invented , and that cases of gender dysphoria in children can be explained by confusion caused by sexuality, confusion caused by neurodiversity, confusion caused by abuse, trauma or mental health conditions but, crucially, never by that child being, either solely or in addition to other factors, transgender. He has described “top surgery” – shorthand that trans men use for a masculinising double-mastectomy – as “bizarre Orwellian newspeak”.

He has described gender-affirming surgeries for adults in Frankenstein terms, bemoaning people like me as “sterile and lifelong patients, many facing catastrophic complications”. I don’t really want to dignify this claim with a serious rebuttal, so suffice to say that regret rates for gender-affirming surgeries consistently hover around a whopping 1%.

Bell’s views are echoed by Julie Bindel, who, reacting to the review, says the idea of trans children is a “crazy fallacy”, calls people like me who believe in gender-affirming healthcare for trans children “fanatics in the grip of a demented doctrine” and likens us to Jimmy Savile, and thanks Cass for the “validation” her report provides. Last month, Bindel attended the conference of the Clinical Advisory Network on Sex and Gender, a gender-critical pressure group of which Bell is a member. In Bindel’s view, the group heroically opposes “the cruelty of inflicting a mass sexual experiment on children”.

These views fundamentally undermine trans people’s identities and the legal basis on which our rights to things like dignity, privacy and medical care are also protected. Failing to identify such extreme opinions and push back on them in a review focused on improving care for gender-questioning children and young people is unforgivable. Giving Cass the benefit of the doubt, perhaps simply stating that trans children and adults exist seemed too basic – but in the clinical and cultural context we’re operating in, it remains vital.

If the Cass review was held under a black light, we would see the fingerprints of anti-trans ideology. I don’t believe Cass shares this way of thinking, I think she believes in evidence-based healthcare and that trans children exist. However, allowing her review to be so heavily influenced by bias is a critical failure that is hers to own.

As her work is used, as it will be, to perpetuate a broader hostile environment towards trans people in the UK, the young people she has tried to help will, understandably, feel betrayed. I take this opportunity to implore her team to keep this in mind as she calls for a similar review of services for 17- to 25 - year-olds and potentially beyond. Trans adults also need holistic, safe care (doesn’t everyone?) but our clinics are in a dire state too, with up to five years to wait for a first appointment. Now, reviews hang over us too – about us but no one knows to what extent with us – that may become Trojan horses for those who would roll back or perhaps eradicate affirming trans healthcare from the NHS altogether.

Dr Cass, appeasement might get you through this short-term discomfort in the media spotlight, but please remember: it isn’t your healthcare, your rights or your everyday dignity they are trying to take away.

Freddy McConnell is a freelance journalist

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Title: relation extraction using large language models: a case study on acupuncture point locations.

Abstract: In acupuncture therapy, the accurate location of acupoints is essential for its effectiveness. The advanced language understanding capabilities of large language models (LLMs) like Generative Pre-trained Transformers (GPT) present a significant opportunity for extracting relations related to acupoint locations from textual knowledge sources. This study aims to compare the performance of GPT with traditional deep learning models (Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT)) in extracting acupoint-related location relations and assess the impact of pretraining and fine-tuning on GPT's performance. We utilized the World Health Organization Standard Acupuncture Point Locations in the Western Pacific Region (WHO Standard) as our corpus, which consists of descriptions of 361 acupoints. Five types of relations ('direction_of,' 'distance_of,' 'part_of,' 'near_acupoint,' and 'located_near') (n= 3,174) between acupoints were annotated. Five models were compared: BioBERT, LSTM, pre-trained GPT-3.5, and fine-tuned GPT-3.5, as well as pre-trained GPT-4. Performance metrics included micro-average exact match precision, recall, and F1 scores. Our results demonstrate that fine-tuned GPT-3.5 consistently outperformed other models in F1 scores across all relation types. Overall, it achieved the highest micro-average F1 score of 0.92. This study underscores the effectiveness of LLMs like GPT in extracting relations related to acupoint locations, with implications for accurately modeling acupuncture knowledge and promoting standard implementation in acupuncture training and practice. The findings also contribute to advancing informatics applications in traditional and complementary medicine, showcasing the potential of LLMs in natural language processing.

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