The consequences of online partisan media
Open accessAbstract
Significance: Popular wisdom suggests that the internet plays a major role in influencing people’s attitudes and behaviors related to politics, such as by providing slanted sources of information. Yet evidence for this proposition is elusive due to methodological difficulties and the multifaceted nature of online media effects. This study breaks ground by demonstrating a nudge-like approach for exploring these effects through a combination of real-world experimentation and computational social science techniques. The results confirm that it is difficult for people to be persuaded by competing media accounts during a contentious election campaign. At the same time, data from a longer time span suggest that the real consequence of online partisan media may be an erosion of trust in mainstream news.
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Populist politics often coupled with right-wing nationalist ideologies artificially divide society into a corrupt elite opposed by the people, which runs counter to pluralistic democracy and undermines citizens' trust in politics and media.
“Likewise, populist politics that are often coupled with right-wing nationalist ideologies, artificially divide society into a corrupt ‘elite’ that is opposed by ‘the people’, which runs counter to the ideals of a pluralistic democracy and undermines citizens’ trust in politics and the media36,37.”
From A systematic review of worldwide causal and correlational evidence on digital media and democracy (2022) · cites this paper as [37]
The Discussion reports suggestive evidence that increased exposure to online partisan news may lower trust in mainstream media. It does not address populism’s division of society into ‘elite’ and ‘people,’ right-wing nationalism, pluralistic democracy, or diminished trust in politics; nor does it attribute the media-trust effect specifically to populist politics. The indexed passages provide section labels but no page numbers. (LLM, conf=0.84)
Experimental — accuracy not yet measuredPersuasion is difficult; the effects of information on beliefs and opinion are often small and temporary and may be especially difficult to change during a contentious presidential election.
“persuasion is simply difficult—the effects of information on beliefs and opinion are often small and temporary and may be especially difficult to change during a contentious presidential election33,40–43”
From Like-minded sources on Facebook are prevalent but not polarizing (2023) · cites this paper as [33]
The Abstract (page unavailable) says people were difficult to persuade with competing media accounts during a contentious election campaign, and the Results (page unavailable) report little measurable change in many attitudes. The paper does not establish that effects on beliefs and opinions are generally temporary; its Results instead describe a lasting decline in media trust, a distinct outcome. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe myth of fake news being common makes people more skeptical of news in general.
“Similar to the idea of perceived polarization increasing actual polarization, the myth of fake news being common makes people more skeptical of news in general (Altay, Berriche, & Acerbi, 2022; Fletcher & Nielsen, 2019; Guess et al., 2021).”
From Social Drivers and Algorithmic Mechanisms on Digital Media. (2024) · cites this paper as Guess et al., 2021
The Results and Discussion report that increased exposure to online partisan news was associated with reduced trust in mainstream media. They do not examine whether believing fake news is common causes that skepticism; the mechanism attributed to this paper is unsupported. (LLM, conf=0.84)
Experimental — accuracy not yet measuredPartisans consume media from across the partisan divide but do not trust what opposing media channels tell them.
“In other words, partisans consume media from across the partisan divide but do not trust in what opposing media channels tell them.”
From How digital media drive affective polarization through partisan sorting (2022) · cites this paper as [96]
The Discussion (page not available) says exposure to cross-cutting information is relatively common. It also reports reduced overall trust in mainstream media and suggests people may discount challenging content. It does not establish that partisans specifically distrust what opposing media channels tell them; that conclusion is more definite and more source-specific than the paper's finding. (LLM, conf=0.86)
Experimental — accuracy not yet measuredA field experiment in the United States that set browser defaults to partisan media outlets found a long-term loss of trust in mainstream media.
“A field experiment in the United States that set browser defaults to partisan media outlets37 found a long-term loss of trust in mainstream media.”
From A systematic review of worldwide causal and correlational evidence on digital media and democracy (2022) · cites this paper as [37]
One field experiment did not find evidence that exposure to partisan online news substantively shifted political opinions but found a long-term loss of trust in mainstream media.
“Furthermore, one other field experiment did not find evidence that exposure to partisan online news substantively shifted political opinions but found a long-term loss of trust in mainstream media37.”
From A systematic review of worldwide causal and correlational evidence on digital media and democracy (2022) · cites this paper as [37]
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References 68 · Cited by 189
Research works printed but not linked: 24
- [2]Z. Tufekci, Twitter and Tear Gas: The Power and Fragility of Networked Protest (Yale University Press, 2017).
- [3]E. Bakshy, S. Messing, L. A. Adamic, Exposure to ideologically diverse news and opinion on Facebook. Science 348, 1130– 1132 (2015). 25953820
- [4]N. Grinberg, K. Joseph, L. Friedland, B. Swire-Thompson, D. Lazer, Fake news on Twitter during the 2016 US presidential election. Science 363, 374– 378 (2019). 30679368
- [5]A. M. Guess, B. Nyhan, J. Reifler, Exposure to untrustworthy websites in the 2016 US election. Nat. Hum. Behav. 4, 472– 480 (2020). 32123342 PMC7239673
- [8]A. M. Guess, (Almost) everything in moderation: New evidence on Americans’ online media diets. Am. J. Polit. Sci., 10.1111/ajps.12589 (2021). · DOI · Paper page
- [9]S. Iyengar, D. R. Kinder, News that Matters: Television and American Opinion (University of Chicago Press, Chicago, 1987).
- [11]K. Arceneaux, M. Johnson, Changing Minds or Changing Channels?: Partisan News in an Age of Choice (Chicago Studies in American Politics, University of Chicago Press, 2013).
- [16]P. F. Lazarsfeld, B. Berelson, H. Gaudet, The People’s Choice: How the Voter Makes up His Mind in a Presidential Campaign (Columbia University Press, New York, 1944).
- [21]R. H. Thaler, C. R. Sunstein, Nudge: Improving Decisions about Health, Wealth, and Happiness (Penguin, 2009).
- [22]P. Barberá, Who leads? Who follows? Measuring issue attention and agenda setting by legislators and the mass public using social media data. Am. Polit. Sci. Rev. 113, 883– 901 (2019). 33303996 PMC7672368
- [26]G. S. Lenz, Follow the Leader? How Voters Respond to Politicians’ Policies and Performance (University of Chicago Press, 2013).
- [30]J. Ladd, Why Americans Hate the Media and How It Matters (Princeton University Press, 2011).
- [33]R. P. Vallone, L. Ross, M. R. Lepper, The hostile media phenomenon: Biased perception and perceptions of media bias in coverage of the Beirut massacre. J. Pers. Soc. Psychol. 49, 577 (1985). 4045697
- [35]M. R. Perryman, Biased gatekeepers? Partisan perceptions of media attention in the 2016 U.S. presidential election. Journal. Stud. 20, 1– 18 (2019).
- [44]A. Coppock, S. J. Hill, L. Vavreck, The small effects of political advertising are small regardless of context, message, sender, or receiver: Evidence from 59 real-time randomized experiments. Sci. Adv. 6, eabc4046 (2020). 32917601 PMC7467695
- [45]S. Munzert, P. Barberá, A. Guess, J. Yang, Do online voter guides empower citizens? Evidence from a field experiment with digital trace data. Publ. Opin. Q., 10.1093/poq/nfaa037 (2021). PMC8130980 34025295 · DOI
- [47]M. Levendusky, How Partisan Media Polarize America (University of Chicago Press, 2013).
- [49]M. Scharkow, F. Mangold, S. Stier, J. Breuer, How social network sites and other online intermediaries increase exposure to news. Proc. Natl. Acad. Sci. U.S.A. 117, 2761– 2763 (2020). 31988122 PMC7022199
- [50]C. R. Sunstein, #Republic: Divided Democracy in the Age of Social Media (Princeton University Press, 2017).
- [51]E. Hargittai, M. Micheli, “ Internet skills and why they matter” in Society and the Internet: How Networks of Information and Communication are Changing Our Lives, M. Graham, W. H. Dutton, Eds. (Oxford University Press, 2019), p. 109.
- [53]M. Prior, Post-Broadcast Democracy: How Media Choice Increases Inequality in Political Involvement and Polarizes Elections (Cambridge University Press, 2007).
- [55]W. Lin, Agnostic notes on regression adjustments to experimental data: Reexamining freedman’s critique. Ann. Appl. Stat. 7, 295, 318 (2013).
- [58]E. Menchen-Trevino, “ Digital trace data and social research: A proactive research ethics” in The Oxford Handbook of Networked Communication, B. F. Welles, S. González-Bailón, Eds. (Oxford University Press, 2018), pp. 1– 20.
- [59]A. Guess, Data from “ The consequences of online partisan media.” GitHub. https://github.com/NetDem-USC/homepage-experiment. Deposited 9 March 2021. PMC8040813 33782116
- Authors
- Guess, A. M., Barberá, P., Munzert, S., & Yang, J.
- Affiliations
- Princeton University (US)
- University of Southern California (US)
- Hertie School (DE)
- University of Illinois Urbana-Champaign (US)
- Corresponding author
- Guess, Andrew M.
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2021-03-29
- Volume
- 118
- Issue
- 14
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC8040813
- OpenAlex
- W3149746057
- Cited by
- 189
- References
- 68
- Citations by year
- 2026: 20 · 2025: 43 · 2024: 39 · 2023: 48 · 2022: 25 · 2021: 12 · 2020: 2
- FWCI
- 42.96
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- OPUS 4 (Zuse Institute Berlin) (submittedVersion)
- Funders
- Volkswagen Foundation (92 143)
- Topics
- Social Sciences › Sociology and Political Science › Media Influence and Politics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Social Media and Politics
- Keywords
- news consumption, media polarization, social media followers, 2018 midterm elections, computational social science
- MeSH terms
- Humans, Mass Media, Surveys and Questionnaires, Dissent and Disputes, Social Media
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