Media bias

 ( Books About Media Bias ) 

Various proposed hypotheses of media bias have included:

• Advertising bias, when stories are selected or slanted to please advertisers.
• Anti-science bias, when stories promote superstition or other non-scientific ideas.
• Concision bias, a tendency to report views that can be summarized succinctly, crowding out more unconventional views that take time to explain.
• Content bias, differential treatment of the parties in political conflicts, where biased news presents only one side of the conflict.
• Corporate bias, when stories are selected or slanted to please corporate owners of media.
• Coverage bias when media choose to report only negative news about one party or ideology
• Decision-making bias, means that the motivation, frame of mind, or beliefs of the journalists will have an impact on their writing. It is generally pejorative.
• Demand-driven bias.
• Demographic bias, where factors such as gender, race, and social and economic status influence reporting and can be a factor in different coverage of various demographic groups.
• Distortion bias, when the fact or reality is distorted or fabricated in the news.
• Episodic framing of television, for example, can lead people to ascribe blame to individuals instead of society, in contrast to thematic framing that leads people to look more at societal causes.
  Shanto Iyengar (1994). 9780226388557, Univ. of Chicago Press. ISBN 9780226388557
• False balance and false equivalence occur when an issue is presented as having equally-compelling reasons on both sides, despite disproportionate amounts of evidence favoring one (also known as undue weight).
• False timeliness, implying that an event is a new event, and thus deriving notability, without addressing past events of the same kind.
• Gatekeeping bias (also known as selectivity or selection bias), when stories are selected or deselected, sometimes on ideological grounds (see spike). It is sometimes also referred to as agenda bias, when the focus is on political actors and whether they are covered based on their preferred policy issues.
• Mainstream bias, a tendency to report what everyone else is reporting, and to avoid stories that will offend anyone. This type of bias can result in the homogenization of information, diminishing diversity in media content and negatively impacting both media consumption and the overall user experience.
  (2025). 9783031560699, Springer Nature Switzerland. ISBN 9783031560699
• Negativity bias (or bad news bias), a tendency to show negative events and portray politics as less of a debate on policy and more of a zero-sum struggle for power. Excessive criticism or negativity can lead to cynicism and disengagement from politics.
  S. Robert Lichter (2014). 9780199793471, Oxford University Press. . ISBN 9780199793471
• Normalcy bias, a bias to represent the abnormal as ordinary
• Partisan bias, a tendency to report to serve particular political party leaning.
• Sensationalism, bias in favor of the exceptional over the ordinary, giving the impression that rare events, such as airplane crashes, are more common than common events, such as automobile crashes. "Hierarchy of death" and "missing white woman syndrome" are examples of this phenomenon.
• Speculative content, when stories focus not on what has occurred, but primarily on what might occur, using words like "could", "might", or "what if", without labeling the article as analysis or opinion.
• Statement bias (also known as tonality bias or presentation bias), when media coverage is slanted towards or against particular actors or issues.
• Structural bias, when an actor or issue receives more or less favorable coverage as a result of News values and media routines, not as the result of ideological decisions (e.g. Incumbent).
• Supply-driven bias
• Tuchman's Law suggests how people overestimate the risk from dangers that are disproportionately discussed in media.
• Ventriloquism, when experts or witnesses are quoted in a way that intentionally voices the author's own opinion.

An ongoing and unpublished research project named "The Media Bias Taxonomy" is attempting to assess the various definitions and meanings of media bias. While still ongoing, it attempts to summarize the domain as the distinct subcategories linguistic bias (encompassing linguistic intergroup bias, framing bias, epistemological bias, bias by semantic properties, and connotation bias), text-level context bias (featuring statement bias, phrasing bias, and spin bias), reporting-level context bias (highlighting selection bias, coverage bias, and proximity bias), cognitive biases (such as selective exposure and partisan bias), and related concepts like framing effects, hate speech, sentiment analysis, and group biases (encompassing gender bias, racial bias, and religion bias). The authors emphasize the complex nature of detecting and mitigating bias across different media content and contexts. additional copy

In the 19th century, journalists began to recognize the concept of unbiased reporting as an integral part of journalistic ethics. This coincided with the rise of journalism as a powerful social force. Even today, the most conscientiously objective cannot avoid accusations of bias.

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.

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.

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.

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.

Demand-driven models evaluate to what extent media bias stems from companies providing consumers what they want. 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.

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.

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 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 chambers.

Fueled by confirmation bias, online 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. Another 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". 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 chambers by interacting with different people and perspectives along with avoiding the temptation of confirmation bias.

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.

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."Barbara Fister, Anti-Social Media: A Review, InsideHigherEd (June 6, 2018).Rose Deller, Book Review: Anti-Social Media: How Facebook Disconnects Us and Undermines Democracy by Siva Vaidhyanathan, LSE Review of Books (October 4, 2018).

In a 2021 report, researchers at the New York University's 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."Paul M. Barrett & Grant Simms, False Accusation: The Unfounded Claim that Social Media Companies Censor Conservatives, Stern Center for Business and Human Rights, New York University (February 2021).Alison Durkee, Are Social Media Companies Biased Against Conservatives? There's No Solid Evidence, Report Concludes, Forbes (February 1, 2021).

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 rightspecifically, 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. 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." These findings held true for both hashtags and links. The study also found that conservative accounts are exposed to substantially more low-credibility content than other accounts.

A 2022 study in 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.

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.

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.