Users choose to engage with more partisan news than they are exposed to on Google Search
Abstract
If popular online platforms systematically expose their users to partisan and unreliable news, they could potentially contribute to societal issues such as rising political polarization 1,2. This concern is central to the 'echo chamber'3-5 and 'filter bubble'6,7 debates, which critique the roles that user choice and algorithmic curation play in guiding users to different online information sources 8-10. These roles can be measured as exposure, defined as the URLs shown to users by online platforms, and engagement, defined as the URLs selected by users. However, owing to the challenges of obtaining ecologically valid exposure data-what real users were shown during their typical platform use-research in this vein typically relies on engagement data 4,8,11-16 or estimates of hypothetical exposure 17-23. Studies involving ecological exposure have therefore been rare, and largely limited to social media platforms 7,24, leaving open questions about web search engines. To address these gaps, we conducted a two-wave study pairing surveys with ecologically valid measures of both exposure and engagement on Google Search during the 2018 and 2020 US elections. In both waves, we found more identity-congruent and unreliable news sources in participants' engagement choices, both within Google Search and overall, than they were exposed to in their Google Search results. These results indicate that exposure to and engagement with partisan or unreliable news on Google Search are driven not primarily by algorithmic curation but by users' own choices.
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Users actively engage with more partisan news than suggested by the Google search algorithm.
“Similarly, users actively engage with more partisan news than suggested by the Google search algorithm (Robertson et al., 2023).”
From Social Drivers and Algorithmic Mechanisms on Digital Media. (2024) · cites this paper as Robertson et al., 2023
Individuals engage with more ideologically congruent and unreliable news sources than they would if they relied only Google Search results.
“Robertson et al. (2023) find that individuals engage with more ideologically congruent and unreliable news sources than they would if they relied only Google Search results (i.e., they choose congruence).”
From How to study democratic backsliding (2024) · cites this paper as Robertson et al., 2023
Despite popular media claims about machine-generated art developing its own unique style, we do not yet have a reliable way of verifying these claims, let alone assessing machine-to-human cultural transmission.
“Despite popular media claims about machine-generated art developing its own unique style 177 , we do not yet have a reliable way of verifying these claims, let alone assessing machine-to-human cultural transmission 107 .”
From Machine culture (2023) · cites this paper as [177]
Cites
Social Media and Fake News in the 2016 Election
Allcott, H., & Gentzkow, M. (2017) · Journal of Economic Perspectives · doi:10.1257/jep.31.2.211 · Cited by 6,767 · see citing works
Motivated Skepticism in the Evaluation of Political Beliefs
Taber, C. S., & Lodge, M. (2006) · American Journal of Political Science · doi:10.1111/j.1540-5907.2006.00214.x · Cited by 4,344 · see citing works
Lazer, D. M. J., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., Metzger, M. J., Nyhan, B., Pennycook, G., Rothschild, D., et al. (2018) · Science · doi:10.1126/science.aao2998 · Cited by 4,160 · see citing works
Self-citationExposure to ideologically diverse news and opinion on Facebook
Bakshy, E., Messing, S., & Adamic, L. A. (2015) · Science · doi:10.1126/science.aaa1160 · Cited by 3,245 · see citing works
Algorithms of Oppression: How Search Engines Reinforce Racism
Noble, S. U. (2020) · doi:10.2307/j.ctt1pwt9w5 · Cited by 2,709 · see citing works
Gillespie, T. (2010) · New Media & Society · doi:10.1177/1461444809342738 · Cited by 2,522 · see citing works
The echo chamber effect on social media
Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2023301118 · Cited by 2,092 · see citing works
Filter Bubbles, Echo Chambers, and Online News Consumption
Flaxman, S., Goel, S., & Rao, J. M. (2016) · Public Opinion Quarterly · doi:10.1093/poq/nfw006 · Cited by 1,992 · see citing works
Beyond misinformation: Understanding and coping with the “post-truth” era.
Lewandowsky, S., Ecker, U. K. H., & Cook, J. (2017) · Journal of Applied Research in Memory and Cognition · doi:10.1016/j.jarmac.2017.07.008 · Cited by 1,985 · see citing works
Red Media, Blue Media: Evidence of Ideological Selectivity in Media Use
Iyengar, S., & Hahn, K. S. (2009) · Journal of Communication · doi:10.1111/j.1460-2466.2008.01402.x · Cited by 1,940 · see citing works
FILTER BUBBLE: Wie wir im Internet entmündigt werden
Pariser, E. (2012) · doi:10.3139/9783446431164 · Cited by 1,849 · see citing works
Fake news on Twitter during the 2016 U.S. presidential election
Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., & Lazer, D. (2019) · Science · doi:10.1126/science.aau2706 · Cited by 1,803 · see citing works
Self-citationLess than you think: Prevalence and predictors of fake news dissemination on Facebook
Guess, A., Nagler, J., & Tucker, J. (2019) · Science Advances · doi:10.1126/sciadv.aau4586 · Cited by 1,652 · see citing works
Memory: A contribution to experimental psychology.
Ebbinghaus, H. (1913) · doi:10.1037/10011-000 · Cited by 1,645 · see citing works
Accessibility of information on the web
Lawrence, S., & Giles, C. L. (1999) · Nature · doi:10.1038/21987 · Cited by 1,371 · see citing works
Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.-F., Breazeal, C., Crandall, J. W., Christakis, N. A., Couzin, I. D., Jackson, M. O., et al. (2019) · Nature · doi:10.1038/s41586-019-1138-y · Cited by 1,364 · see citing works
Self-citationProceedings of the ACM on Human-Computer Interaction
Karahalios, K. G., Monroy‐Hernández, A., Lampinen, A., & Fitzpatrick, G. (2018) · Cited by 1,070 · see citing works
The future of sex and gender in psychology: Five challenges to the gender binary.
Hyde, J. S., Bigler, R. S., Joel, D., Tate, C. C., & van Anders, S. M. (2018) · American Psychologist · doi:10.1037/amp0000307 · Cited by 1,012 · see citing works
Parry, D. A., Davidson, B. I., Sewall, C. J. R., Fisher, J. T., Mieczkowski, H., & Quintana, D. S. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01117-5 · Cited by 793 · see citing works
Algorithmic Accountability: Journalistic investigation of computational power structures
Diakopoulos, N. (2015) · Digital Journalism · doi:10.1080/21670811.2014.976411 · Cited by 733 · see citing works
Shaping the Web: Why the Politics of Search Engines Matters
Introna, L. D., & Nissenbaum, H. F. (2017) · Routledge eBooks · doi:10.4324/9781315259697-19 · Cited by 717 · see citing works
In Google We Trust: Users’ Decisions on Rank, Position, and Relevance
Pan, B., Hembrooke, H., Joachims, T., Lorigo, L., Gay, G., & Granka, L. (2007) · Journal of Computer-Mediated Communication · doi:10.1111/j.1083-6101.2007.00351.x · Cited by 715 · see citing works
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
Joachims, T., Granka, L. A., Pan, B., Hembrooke, H. A., Radlinski, F., & Gay, G. K. (2007) · ACM Transactions on Information Systems · doi:10.1145/1229179.1229181 · Cited by 678 · see citing works
Exposure to untrustworthy websites in the 2016 US election
Guess, A. M., Nyhan, B., & Reifler, J. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0833-x · Cited by 587 · see citing works
The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections
Epstein, R., & Robertson, R. E. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1419828112 · Cited by 578 · see citing works
Self-citationEvaluating the fake news problem at the scale of the information ecosystem
Allen, J., Howland, B., Mobius, M., Rothschild, D., & Watts, D. J. (2020) · Science Advances · doi:10.1126/sciadv.aay3539 · Cited by 528 · see citing works
Klar, S., & Krupnikov, Y. (2016) · Cambridge University Press eBooks · doi:10.1017/cbo9781316471050 · Cited by 424 · see citing works
Measuring personalization of web search
Hannak, A., Sapiezynski, P., Molavi Kakhki, A., Krishnamurthy, B., Lazer, D., Mislove, A., & Wilson, C. (2013) · Proceedings of the 22nd international conference on World Wide Web · doi:10.1145/2488388.2488435 · Cited by 396 · see citing works
Self-citationThe Googlization of Everything: (And Why We Should Worry)
Vaidhyanathan, S. (2011) · doi:10.1525/9780520948693 · Cited by 362 · see citing works
(Almost) Everything in Moderation: New Evidence on Americans' Online Media Diets
Guess, A. M. (2021) · American Journal of Political Science · doi:10.1111/ajps.12589 · Cited by 342 · see citing works
Auditing Partisan Audience Bias within Google Search
Robertson, R. E., Jiang, S., Joseph, K., Friedland, L., Lazer, D., & Wilson, C. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274417 · Cited by 209 · see citing works
Self-citationReeves, B., Ram, N., Robinson, T. N., Cummings, J. J., Giles, C. L., Pan, J., Chiatti, A., Cho, M., Roehrick, K. C., Yang, X., et al. (2019) · Hum. Comput. Interact. · doi:10.1080/07370024.2019.1578652 · Cited by 199 · see citing works
The consequences of online partisan media
Guess, A. M., Barberá, P., Munzert, S., & Yang, J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2013464118 · Cited by 189 · see citing works
Algorithmically recognizable: Santorum’s Google problem, and Google’s Santorum problem
Gillespie, T. (2017) · Information, Communication & Society · doi:10.1080/1369118x.2016.1199721 · Cited by 178 · see citing works
Examining the consumption of radical content on YouTube
Hosseinmardi, H., Ghasemian, A., Clauset, A., Mobius, M., Rothschild, D. M., & Watts, D. J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2101967118 · Cited by 171 · see citing works
Political Participation of Women of Color: An Intersectional Analysis
Brown, N. E. (2014) · Journal of Women Politics & Policy · doi:10.1080/1554477x.2014.955406 · Cited by 169 · see citing works
Partisan selective exposure in online news consumption: evidence from the 2016 presidential campaign
Peterson, E., Goel, S., & Iyengar, S. (2019) · Political Science Research and Methods · doi:10.1017/psrm.2019.55 · Cited by 156 · see citing works
Measuring algorithmically infused societies
Wagner, C., Strohmaier, M., Olteanu, A., Kıcıman, E., Contractor, N., & Eliassi-Rad, T. (2021) · Nature · doi:10.1038/s41586-021-03666-1 · Cited by 144 · see citing works
Measuring the news and its impact on democracy
Watts, D. J., Rothschild, D. M., & Mobius, M. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912443118 · Cited by 121 · see citing works
Trielli, D., & Diakopoulos, N. A. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300683 · Cited by 111 · see citing works
Digital Technologies and Selective Exposure: How Choice and Filter Bubbles Shape News Media Exposure
Cardenal, A. S., Paredes, C. A., Galais, C., & Pérez–Montoro, M. (2019) · The International Journal of Press/Politics · doi:10.1177/1940161219862988 · Cited by 110 · see citing works
Search bias quantification: investigating political bias in social media and web search
Kulshrestha, J., Eslami, M., Messias, J., Zafar, M. B., Ghosh, S., Gummadi, K. P., & Karahalios, K. G. (2018) · Information Retrieval · doi:10.1007/s10791-018-9341-2 · Cited by 103 · see citing works
Are We Exposed to the Same “News” in the News Feed?
Bechmann, A., & Nielbo, K. L. (2018) · Digital Journalism · doi:10.1080/21670811.2018.1510741 · Cited by 101 · see citing works
Suppressing the Search Engine Manipulation Effect (SEME)
Epstein, R., Robertson, R. E., Lazer, D. M. J., & Wilson, C. (2017) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3134677 · Cited by 90 · see citing works
Self-citationPost Post-Broadcast Democracy? News Exposure in the Age of Online Intermediaries
Stier, S., Mangold, F., Scharkow, M., & Breuer, J. (2021) · American Political Science Review · doi:10.1017/s0003055421001222 · Cited by 84 · see citing works
Fletcher, R., Kalogeropoulos, A., & Nielsen, R. K. (2021) · New Media & Society · doi:10.1177/14614448211027393 · Cited by 79 · see citing works
Wojcieszak, M., Menchen-Trevino, E., Gonçalves, J., & Weeks, B. E. (2021) · The International Journal of Press/Politics · doi:10.1177/19401612211009160 · Cited by 72 · see citing works
Quantifying partisan news diets in Web and TV audiences
Muise, D., Hosseinmardi, H., Howland, B., Möbius, M., Rothschild, D. M., & Watts, D. J. (2022) · Science Advances · doi:10.1126/sciadv.abn0083 · Cited by 72 · see citing works
Neutral bots probe political bias on social media
Chen, W., Pacheco, D. F., Yang, K.-C., & Menczer, F. (2021) · RePEc: Research Papers in Economics · Cited by 68 · see citing works
Neutral bots probe political bias on social media
Chen, W., Pacheco, D. F., Yang, K.-C., & Menczer, F. (2021) · Nature Communications · doi:10.1038/s41467-021-25738-6 · Cited by 66 · see citing works
Algorithmic Amplification of Politics on Twitter
Huszár, F., Ktena, S. I., O’Brien, C., Belli, L., Schlaikjer, A., & Hardt, M. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2025334119 · Cited by 65 · see citing works
Partisan search behavior and Google results in the 2018 U.S. midterm elections
Trielli, D., & Diakopoulos, N. A. (2020) · Information Communication & Society · doi:10.1080/1369118x.2020.1764605 · Cited by 61 · see citing works
Democratic Consequences of Incidental Exposure to Political Information: A Meta-Analysis
Nanz, A., & Matthes, J. (2022) · Journal of Communication · doi:10.1093/joc/jqac008 · Cited by 61 · see citing works
Political Polarization in Online News Consumption
Garimella, K., Smith, T., Weiss, R., & West, R. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v15i1.18049 · Cited by 56 · see citing works
Auditing local news presence on Google News
Fischer, S., Jaidka, K., & Yphtach Lelkes, B. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-00954-0 · Cited by 52 · see citing works
Self-imposed filter bubbles: Selective attention and exposure in online search
Ekström, A. G., Niehorster, D. C., & Olsson, E. J. (2022) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2022.100226 · Cited by 45 · see citing works
Zuckerman, E. (2021) · Information Communication & Society · doi:10.1080/1369118x.2021.1942513 · Cited by 44 · see citing works
Metaxa, D., Park, J.-S., Landay, J. A., & Hancock, J. T. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359231 · Cited by 39 · see citing works
The case for voter-centered audits of search engines during political elections
Mustafaraj, E., Lurie, E., & Devine, C. (2020) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3351095.3372835 · Cited by 28 · see citing works
Searching differently? How political attitudes impact search queries about political issues
van Hoof, M., Meppelink, C. S., Möller, J. E., & Trilling, D. (2022) · New Media & Society · doi:10.1177/14614448221104405 · Cited by 25 · see citing works
Kawakami, A., Umarova, K., & Mustafaraj, E. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7352 · Cited by 12 · see citing works
Can “Googling” correct misbelief? Cognitive and affective consequences of online search
Kobayashi, T., Taka, F., & Suzuki, T. (2021) · PLoS ONE · doi:10.1371/journal.pone.0256575 · Cited by 12 · see citing works
Pivoted Text Scaling for Open-Ended Survey ResponsesPreprint
Hobbs, W. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3044864 · Cited by 5 · see citing works
yinleon/LocalNewsDataset: Initial release
Yin, L. (2018) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1345143 · Cited by 3 · see citing works
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Social Drivers and Algorithmic Mechanisms on Digital Media.
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“Similarly, users actively engage with more partisan news than suggested by the Google search algorithm (Robertson et al., 2023).”
BackgroundBrinkmann, L., Baumann, F., Bonnefon, J.-F., Derex, M., Müller, T. F., Nussberger, A.-M., Czaplicka, A., Acerbi, A., Griffiths, T. L., Henrich, J., et al. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01742-2 · Cited by 132 · see citing works
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How to study democratic backsliding
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- Authors
- Robertson, R. E., Green, J., Ruck, D. J., Ognyanova, K., Wilson, C., & Lazer, D.
- Affiliations
- Northeastern University (US)
- Rutgers, The State University of New Jersey (US)
- Corresponding author
- Robertson, Ronald E.
- Venue
- Nature
- Published
- 2023-05-24
- Volume
- 618
- Issue
- 7964
- Pages
- 342-348
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W4377941795
- Semantic Scholar
- ebb037b9ccd4a510ec8ee651ba53417b233cc40c
- Cited by
- 126
- References
- 64
- Citations by year
- 2026: 38 · 2025: 35 · 2024: 29 · 2023: 23 · 2021: 1
- FWCI
- 53.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Sociology and Political Science › Media Influence and Politics
- Keywords
- Google search, partisan news, echo chambers, filter bubbles, political polarization, user engagement, algorithmic curation, 2020 US presidential election
- MeSH terms
- Information Sources, Algorithms, Choice Behavior, Humans, Politics, Prejudice, Surveys and Questionnaires, United States, Reproducibility of Results, Search Engine
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