Editing Media bias (section)

== Structural (Non-ideological) biases ==
While most accusations of bias tend to revolve around ideological disagreements, other forms of bias are cast as structural in nature. There is little agreement on how they operate or originate but some involve economics, government policies, norms, and the individual creating the news.<ref name=":13">{{cite book |last=Lichter |first=S. Robert |author-link=Samuel Robert Lichter |title=The Oxford Handbook of Political Communication |date=2018 |publisher=[[Oxford University Press]] |isbn=9780199984350 |editor1-last=Kenski |editor1-first=Kate |series=Oxford Handbooks Online |location=Oxford; New York |pages=405 |chapter=Theories of Media Bias |doi=10.1093/oxfordhb/9780199793471.013.44 |oclc=959803808 |quote= |editor2-last=Jamieson |editor2-first=Kathleen Hall |editor2-link=Kathleen Hall Jamieson}}</ref> Some examples, according to Cline (2009) include commercial bias, temporal bias, visual bias, bad news bias, narrative bias, status quo bias, fairness bias, expediency bias, class bias and glory bias (or the tendency to glorify the reporter).<ref>{{Cite book |last=Cline |first=Andrew |url=https://www.worldcat.org/title/251216055 |title=21st century communication: a reference handbook |date=2009 |publisher=Sage |isbn=978-1-4129-5030-5 |editor-last=Eadie |editor-first=William F. |series=21st century reference series |location=Los Angeles |chapter=53: Bias |oclc=251216055}}</ref>

There is also a growing [[economics]] literature on mass media bias, both on the theoretical and the empirical side. On the theoretical side the focus is on understanding to what extent the political positioning of mass media outlets is mainly driven by demand or supply factors. This literature was surveyed by [[Andrea Prat]] of Columbia University and David Stromberg of Stockholm University in 2013.<ref>{{Cite book |last1=Prat |first1=Andrea |title=Advances in Economics and Econometrics |last2=Strömberg |first2=David |year=2013 |isbn=9781139060028 |pages=135–187 |chapter=The Political Economy of Mass Media |doi=10.1017/CBO9781139060028.004 |s2cid=15050221}}</ref>

=== Supply-driven bias ===
When an organization prefers consumers to take particular actions, this would be supply-driven bias.

Implications of supply-driven bias:<ref name=":5">{{Cite book |last1=Gentzkow |first1=Matthew |title=Handbook of Media Economics |last2=Shapiro |first2=Jesse M. |last3=Stone |first3=Daniel F. |date=2015-01-01 |publisher=North-Holland |isbn=978-0-444-63691-1 |editor-last=Anderson |editor-first=Simon P. |volume=1 |pages=623–645 |chapter=Chapter 14 – Media Bias in the Marketplace: Theory |doi=10.1016/b978-0-444-63685-0.00014-0 |access-date=2022-03-30 |editor-last2=Waldfogel |editor-first2=Joel |editor-last3=Strömberg |editor-first3=David |chapter-url=https://www.sciencedirect.com/science/article/pii/B9780444636850000140 |s2cid=8736042}}</ref>
* Supply-side incentives are able to control and affect consumers. Strong persuasive incentives can even be more powerful than profit motivation.
* Competition leads to decreased bias and hinders the impact of persuasive incentives. And it tends to make the results more responsive to consumer demand.
* Competition can improve consumer treatment, but it may affect the total surplus due to the ideological payoff of the owners.

An example of supply-driven bias is Zinman and Zitzewitz's study of snowfall reporting. Ski attractions tend to be biased in snowfall reporting, reporting higher snowfall than official forecasts.<ref name=":4">{{Cite journal |last1=Raymond |first1=Collin |last2=Taylor |first2=Sarah |date=2021-04-01 |title="Tell all the truth, but tell it slant": Documenting media bias |url=https://www.sciencedirect.com/science/article/pii/S0167268120303383 |journal=[[Journal of Economic Behavior & Organization]] |language=en |volume=184 |pages=670–691 |doi=10.1016/j.jebo.2020.09.021 |issn=0167-2681 |s2cid=228814765}}</ref>{{Better source needed|reason=The current source may not be sufficiently reliable as it had only 6 citations as of March 2024 ([[WP:NOTRS]]).|date=March 2024}}

David Baron suggests a game-theoretic model of mass media behaviour in which, given that the pool of journalists systematically leans towards the left or the right, mass media outlets maximise their profits by providing content that is biased in the same direction as their employees.<ref>{{Cite journal |last1=Baron |first1=David P. |date=2004 |title=Persistent Media Bias |url=http://www.wallis.rochester.edu/conference11/mediabias.pdf |journal=SSRN |doi=10.2139/ssrn.516006 |s2cid=154786996 |ssrn=516006 |archive-url=https://web.archive.org/web/20171019013018/http://www.wallis.rochester.edu/conference11/mediabias.pdf |archive-date=2017-10-19}} Later published as:<br />{{Cite journal |last1=Baron |first1=David P. |year=2006 |title=Persistent Media Bias |journal=Journal of Public Economics |volume=90 |issue=1–2 |pages=1–36 |doi=10.1016/j.jpubeco.2004.10.006}}</ref>

[[Edward S. Herman|Herman]] and [[Noam Chomsky|Chomsky]] ([[Manufacturing Consent|1988]]) cite supply-driven bias including around the use of official sources, funding from advertising, efforts to discredit independent media ("flak"), and "[[anti-communist]]" ideology, resulting in news in favor of U.S. corporate interests.<ref>{{Cite journal |last1=Mullen |first1=Andrew |last2=Klaehn |first2=Jeffery |year=2010 |title=The Herman-Chomsky Propaganda Model: A Critical Approach to Analysing Mass Media Behaviour |url=http://www.fifth-estate-online.co.uk:80/wp-content/uploads/2011/07/Mullen-Klaehn-Sociology-Compass-essay.pdf |journal=Sociology Compass |volume=4 |issue=4 |pages=215–229 |citeseerx=10.1.1.458.4091 |doi=10.1111/j.1751-9020.2010.00275.x |archive-url=https://web.archive.org/web/20120617025333/http://www.fifth-estate-online.co.uk:80/wp-content/uploads/2011/07/Mullen-Klaehn-Sociology-Compass-essay.pdf |archive-date=2012-06-17}}</ref>

=== Demand-driven bias ===
Demand from media consumer for a particular type of bias is known as demand-driven bias. Consumers tend to favor a biased media based on their preferences, an example of [[confirmation bias]].<ref name=":5" />

There are three major factors that make this choice for consumers:
* Delegation, which takes a filtering approach to bias.
* Psychological utility, "consumers get direct utility from news whose bias matches their own prior beliefs."
* Reputation, consumers will make choices based on their prior beliefs and the reputation of the media companies.

Demand-side incentives are often not related to distortion. Competition can still affect the welfare and treatment of consumers, but it is not very effective in changing bias compared to the supply side.<ref name=":5" />

In demand-driven bias, preferences and attitudes of readers can be monitored on social media, and mass media write news that caters to readers based on them. Mass media skew news driven by viewership and profits, leading to the media bias. And readers are also easily attracted to lurid news, although they may be biased and not true enough.

Dong, Ren, and Nickerson investigated Chinese stock-related news and weibos in 20132014 from Sina Weibo and Sina Finance (4.27 million pieces of news and 43.17 million weibos) and found that news that aligns with Weibo users' beliefs are more likely to attract readers. Also, the information in biased reports also influences the decision-making of the readers.<ref>{{Cite journal |last1=Dong |first1=H. |last2=Ren |first2=J. |last3=Nickerson |first3=J. V. |date=January 2018 |title=Be Careful What You Read: Evidence of demand-driven media bias |url=https://par.nsf.gov/biblio/10098190-careful-what-you-read-evidence-demand-driven-media-bias |journal=Proceedings of the Americas Conference on Information Systems |language=en}}</ref>

In Raymond and Taylor's test of weather forecast bias, they investigated weather reports of the New York Times during the games of the baseball team the Giants from 1890 to 1899. Their findings suggest that the New York Times produce biased weather forecast results depending on the region in which the Giants play. When they played at home in Manhattan, reports of sunny days predicting increased. From this study, Raymond and Taylor found that bias pattern in New York Times weather forecasts was consistent with demand-driven bias.<ref name=":4" />{{Better source needed|reason=The current source may not be sufficiently reliable as it had only 6 citations as of March 2024 ([[WP:NOTRS]]).|date=March 2024}}

Sendhil Mullainathan and Andrei Shleifer of Harvard University constructed a behavioural model in 2005, which is built around the assumption that readers and viewers hold beliefs that they would like to see confirmed by news providers, which they argue the market then provides.<ref>{{Cite journal |last1=Mullainathan |first1=Sendhil |last2=Shleifer |first2=Andrei |year=2005 |title=The Market for News |url=http://nrs.harvard.edu/urn-3:HUL.InstRepos:33078973 |journal=American Economic Review |volume=95 |issue=4 |pages=1031–1053 |doi=10.1257/0002828054825619 |jstor=4132704}}</ref>

Demand-driven models evaluate to what extent media bias stems from companies providing consumers what they want.<ref>{{Cite journal |last1=Gentzkow |first1=Matthew |last2=Shapiro |first2=Jesse M. |year=2006 |title=Media Bias and Reputation |url=https://www.brown.edu/Research/Shapiro/pdfs/bias.pdf |journal=Journal of Political Economy |volume=114 |issue=2 |pages=280–316 |doi=10.1086/499414 |s2cid=222429768}}</ref> Stromberg posits that because wealthier viewers result in more advertising revenue, the media as a result ends up targeted to whiter and more conservative consumers while wealthier urban markets may be more liberal and produce an opposite effect in newspapers in particular.<ref>{{cite thesis |last=Strömberg |first=David |date=November 1999 |title=The Politics of Public Spending |type=PhD |oclc=42036086 |publisher=Princeton University |url=http://people.su.se/~dstro/chapter1.pdf |archive-date=15 April 2010 |archive-url=https://web.archive.org/web/20100415122740/http://people.su.se/~dstro/chapter1.pdf |access-date=19 January 2021}}</ref>

=== Social media ===
Perceptions of media bias may also be related to the rise of social media. The rise of social media has undermined the economic model of traditional media. The number of people who rely upon social media has increased and the number who rely on print news has decreased.<ref name="West">{{cite news |last1=West |first1=Darrell M. |date=18 December 2017 |title=How to combat fake news and disinformation |url=https://www.brookings.edu/research/how-to-combat-fake-news-and-disinformation/ |work=Brookings}}</ref>
Studies of social media and [[Disinformation attack|disinformation]] suggest that the political economy of social media platforms has led to a commodification of information on social media.  Messages are prioritized and rewarded based on their virality and shareability rather than their truth,<ref name="Gundersen">{{cite journal |last1=Gundersen |first1=Torbjørn |last2=Alinejad |first2=Donya |last3=Branch |first3=T.Y. |last4=Duffy |first4=Bobby |last5=Hewlett |first5=Kirstie |last6=Holst |first6=Cathrine |last7=Owens |first7=Susan |last8=Panizza |first8=Folco |last9=Tellmann |first9=Silje Maria |last10=van Dijck |first10=José |last11=Baghramian |first11=Maria |date=17 October 2022 |title=A New Dark Age? Truth, Trust, and Environmental Science |url=https://www.annualreviews.org/doi/full/10.1146/annurev-environ-120920-015909 |journal=Annual Review of Environment and Resources |language=en |volume=47 |issue=1 |pages=5–29 |doi=10.1146/annurev-environ-120920-015909 |issn=1543-5938 |s2cid=250659393 |access-date=7 June 2023 |hdl-access=free |hdl=10852/99734}}</ref> promoting radical, shocking click-bait content.<ref name="Brogly">{{cite journal |last1=Brogly |first1=Chris |last2=Rubin |first2=Victoria L. |date=2018 |title=Detecting Clickbait: Here's How to Do It / Comment détecter les pièges à clic |url=https://muse.jhu.edu/article/743050 |journal=Canadian Journal of Information and Library Science |volume=42 |issue=3 |pages=154–175 |issn=1920-7239}}</ref> 
Social media influences people in part because of psychological tendencies to accept incoming information, to take feelings as evidence of truth, and to not check assertions against facts and memories.<ref name="Brashier">{{cite journal |last1=Brashier |first1=Nadia M. |last2=Marsh |first2=Elizabeth J. |date=4 January 2020 |title=Judging Truth |journal=Annual Review of Psychology |language=en |volume=71 |issue=1 |pages=499–515 |doi=10.1146/annurev-psych-010419-050807 |issn=0066-4308 |pmid=31514579 |s2cid=202569061 |doi-access=free}}</ref>

Media bias in social media is also reflected in [[hostile media effect]]. Social media has a place in disseminating news in modern society, where viewers are exposed to other people's comments while reading news articles. In their 2020 study, Gearhart and her team showed that viewers' perceptions of bias increased and perceptions of credibility decreased after seeing comments with which they held different opinions.<ref>{{Cite journal |last1=Gearhart |first1=Sherice |last2=Moe |first2=Alexander |last3=Zhang |first3=Bingbing |date=2020-03-05 |title=Hostile media bias on social media: Testing the effect of user comments on perceptions of news bias and credibility |journal=Human Behavior and Emerging Technologies |volume=2 |issue=2 |pages=140–148 |doi=10.1002/hbe2.185 |issn=2578-1863 |s2cid=216195890 |doi-access=free}}</ref>

Within the United States, [[Pew Research Center]] reported that 64% of Americans believed that social media had a toxic effect on U.S. society and culture in July 2020. Only 10% of Americans believed that it had a positive effect on society. Some of the main concerns with social media lie with the spread of [[Disinformation attack|deliberately false information]] and the spread of hate and extremism. Social scientist experts explain the growth of misinformation and hate as a result of the increase in [[Echo chamber (media)|echo chambers]].<ref>{{Cite web |last=Auxier |first=Brooke |date=15 October 2020 |title=64% of Americans say social media have a mostly negative effect on the way things are going in the U.S. today |url=https://www.pewresearch.org/fact-tank/2020/10/15/64-of-americans-say-social-media-have-a-mostly-negative-effect-on-the-way-things-are-going-in-the-u-s-today/ |access-date=19 January 2021 |website=Pew Research Center}}</ref>

Fueled by confirmation bias, online [[Echo chamber (media)|echo chambers]] allow users to be steeped within their own ideology. Because social media is tailored to your interests and your selected friends, it is an easy outlet for political echo chambers.<ref>{{Cite journal |last=Peck |first=Andrew |date=2020 |title=A Problem of Amplification: Folklore and Fake News in the Age of Social Media |url=https://www.jstor.org/stable/10.5406/jamerfolk.133.529.0329 |journal=The Journal of American Folklore |volume=133 |issue=529 |pages=329–351 |doi=10.5406/jamerfolk.133.529.0329 |issn=0021-8715 |jstor=10.5406/jamerfolk.133.529.0329 |s2cid=243130538}}</ref> Another [[Pew Research Center|Pew Research]] poll in 2019 showed that 28% of US adults "often" find their news through social media, and 55% of US adults get their news from social media either "often" or "sometimes".<ref>{{cite news |last1=Suciu |first1=Peter |date=11 October 2019 |title=More Americans Are Getting Their News From Social Media |url=https://www.forbes.com/sites/petersuciu/2019/10/11/more-americans-are-getting-their-news-from-social-media/?sh=765d27803e17 |access-date=19 January 2021 |work=[[Forbes]]}}</ref> Additionally, more people are reported as going to social media for their news as the [[COVID-19 pandemic]] has restricted politicians to online campaigns and social media live streams. GCF Global encourages online users to avoid [[Echo chamber (media)|echo chambers]] by interacting with different people and perspectives along with avoiding the temptation of confirmation bias.<ref>{{Cite web |date=2020-09-23 |title=Online Echo Chambers are Deepening America's Ideological Divide |url=http://www.mediafiledc.com/online-echo-chambers-are-deepening-americas-ideological-divide/ |access-date=2020-12-07 |website=MediaFile |language=en-US}}</ref><ref>{{Cite web |title=Digital Media Literacy: What is an Echo Chamber? |url=https://edu.gcfglobal.org/en/digital-media-literacy/what-is-an-echo-chamber/1/ |access-date=2020-12-07 |website=GCFGlobal.org |language=en}}</ref>

Yu-Ru and Wen-Ting's research looks into how liberals and conservatives conduct themselves on Twitter after three mass shooting events. Although they would both show negative emotions towards the incidents they differed in the narratives they were pushing. Both sides would often contrast in what the root cause was along with who is deemed the victims, heroes, and villain/s. There was also a decrease in any conversation that was considered proactive.<ref>{{Cite journal |last1=Lin |first1=Yu-Ru |last2=Chung |first2=Wen-Ting |date=2020-08-03 |title=The dynamics of Twitter users' gun narratives across major mass shooting events |journal=Humanities and Social Sciences Communications |volume=7 |issue=1 |doi=10.1057/s41599-020-00533-8 |issn=2662-9992 |s2cid=220930950 |doi-access=free}}</ref>

Media scholar [[Siva Vaidhyanathan]], in his book ''Anti-Social Media: How Facebook Disconnects Us and Undermines Democracy'' (2018), argues that on social media networks, the most emotionally charged and polarizing topics usually predominate, and that "If you wanted to build a machine that would distribute propaganda to millions of people, distract them from important issues, energize hatred and bigotry, erode social trust, undermine journalism, foster doubts about science, and engage in massive surveillance all at once, you would make something a lot like [[Facebook]]."<ref>Barbara Fister, [https://www.insidehighered.com/blogs/library-babel-fish/anti-social-media-review Anti-Social Media: A Review], ''InsideHigherEd'' (June 6, 2018).</ref><ref>Rose Deller, [https://blogs.lse.ac.uk/lsereviewofbooks/2018/10/04/book-review-anti-social-media-how-facebook-disconnects-us-and-undermines-democracy-by-siva-vaidhyanathan/ Book Review: Anti-Social Media: How Facebook Disconnects Us and Undermines Democracy by Siva Vaidhyanathan], ''LSE Review of Books'' (October 4, 2018).</ref>

In a 2021 report, researchers at the [[New York University]]'s [[NYU Stern Center for Business and Human Rights|Stern Center for Business and Human Rights]] found that Republicans' frequent argument that social media companies like Facebook and Twitter have an "anti-conservative" bias is false and lacks any reliable evidence supporting it; the report found that right-wing voices are in fact dominant on social media and that the claim that these platforms have an anti-conservative lean "is itself a form of [[Disinformation attack|disinformation]]."<ref>Paul M. Barrett & Grant Simms, [https://static1.squarespace.com/static/5b6df958f8370af3217d4178/t/6011e68dec2c7013d3caf3cb/1611785871154/NYU+False+Accusation+report_FINAL.pdf False Accusation: The Unfounded Claim that Social Media Companies Censor Conservatives], [[NYU Stern Center for Business and Human Rights|Stern Center for Business and Human Rights]], [[New York University]] (February 2021).</ref><ref>Alison Durkee, [https://www.forbes.com/sites/alisondurkee/2021/02/01/are-social-media-companies-biased-against-conservatives-theres-no-solid-evidence-report-concludes/ Are Social Media Companies Biased Against Conservatives? There's No Solid Evidence, Report Concludes], ''Forbes'' (February 1, 2021).</ref>

A 2021 study in ''[[Nature Communications]]'' examined political bias on social media by assessing the degree to which Twitter users were exposed to content on the left and right{{snd}}specifically, exposure on the home timeline (the "news feed"). The study found that conservative Twitter accounts are exposed to content on the right, whereas liberal accounts are exposed to moderate content, shifting those users' experiences toward the political center.<ref name="Chen">{{Cite journal |last1=Chen |first1=Wen |last2=Pacheco |first2=Diogo |last3=Yang |first3=Kai-Cheng |last4=Menczer |first4=Filippo |date=2021-09-22 |title=Neutral bots probe political bias on social media |journal=Nature Communications |language=en |volume=12 |issue=1 |pages=5580 |arxiv=2005.08141 |bibcode=2021NatCo..12.5580C |doi=10.1038/s41467-021-25738-6 |issn=2041-1723 |pmc=8458339 |pmid=34552073 |s2cid=235755530}}</ref> The study determined: "Both in terms of information to which they are exposed and content they produce, drifters initialized with Right-leaning sources stay on the conservative side of the political spectrum. Those initialized with Left-leaning sources, on the other hand, tend to drift toward the political center: they are exposed to more conservative content and even start spreading it."<ref name="Chen" /> These findings held true for both hashtags and links.<ref name="Chen" /> The study also found that conservative accounts are exposed to substantially more low-credibility content than other accounts.<ref name="Chen" />

A 2022 study in ''[[Proceedings of the National Academy of Sciences of the United States of America|PNAS]],'' using a long-running massive-scale randomized experiment, found that the political right enjoys higher algorithmic amplification than the political left in six out of seven countries studied. In the US, algorithmic amplification favored right-leaning news sources.<ref>{{Cite journal |last1=Huszár |first1=Ferenc |last2=Ktena |first2=Sofia Ira |last3=O’Brien |first3=Conor |last4=Belli |first4=Luca |last5=Schlaikjer |first5=Andrew |last6=Hardt |first6=Moritz |date=2022 |title=Algorithmic amplification of politics on Twitter |journal=Proceedings of the National Academy of Sciences |language=en |volume=119 |issue=1 |arxiv=2110.11010 |bibcode=2022PNAS..11925334H |doi=10.1073/pnas.2025334119 |issn=0027-8424 |pmc=8740571 |pmid=34934011 |doi-access=free}}</ref>

Media bias is also reflected in search systems in social media. Kulshrestha and her team found through research in 2018 that the top-ranked results returned by these search engines can influence users' perceptions when they conduct searches for events or people, which is particularly reflected in political bias and polarizing topics.<ref>{{Cite journal |last1=Kulshrestha |first1=Juhi |last2=Eslami |first2=Motahhare |last3=Messias |first3=Johnnatan |last4=Zafar |first4=Muhammad Bilal |last5=Ghosh |first5=Saptarshi |last6=Gummadi |first6=Krishna P. |last7=Karahalios |first7=Karrie |year=2019 |title=Search bias quantifcation: investigating political bias in social media and web search |url=https://link.springer.com/content/pdf/10.1007/s10791-018-9341-2.pdf |journal=Information Retrieval Journal (2019) 22:188–227 |volume=22 |issue=1–2 |pages=188–227 |doi=10.1007/s10791-018-9341-2 |s2cid=52059050 |via=}}</ref>

=== Language ===
Tanya Pamplone warns that since much of international journalism takes place in English, there can be instances where stories and journalists from countries where English is not taught have difficulty entering the global conversation.<ref>{{Cite web |last=Pampalone |first=Tanya |date=September 27, 2019 |title=Watch Your Language: How English is Skewing the Global News Narrative |url=https://gijn.org/stories/watch-your-language-how-english-is-skewing-the-global-news-narrative/ |access-date=2024-02-22 |website=[[Global Investigative Journalism Network]] |language=en-US}}</ref>

Language may also introduce a more subtle form of bias. The selection of metaphors and analogies, or the inclusion of personal information in one situation but not another can introduce bias, such as a gender bias.<ref>{{cite journal |last=Burke |first=Cindy |author2=Mazzarella, Sharon R |year=2008 |title=A Slightly New Shade of Lipstick": Gendered Mediation in Internet News Stories |journal=[[Women's Studies in Communication]] |volume=31 |issue=3 |page=395 |doi=10.1080/07491409.2008.10162548 |s2cid=143545017}}</ref>