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

polarization1,2. This concern is central to the ‘echo chamber’3,4,5 and ‘filter bubble’6,7 debates, which critique the roles that user choice and algorithmic curation play in guiding users

to different online information sources8,9,10. 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

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. Access through your institution Buy or subscribe This is a preview of subscription content,

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institutional subscriptions * Read our FAQs * Contact customer support SIMILAR CONTENT BEING VIEWED BY OTHERS LIKE-MINDED SOURCES ON FACEBOOK ARE PREVALENT BUT NOT POLARIZING Article Open

access 27 July 2023 IDENTITY EFFECTS IN SOCIAL MEDIA Article 10 November 2022 EXPOSURE TO UNTRUSTWORTHY WEBSITES IN THE 2020 US ELECTION Article 13 April 2023 DATA AVAILABILITY Owing to

privacy concerns and IRB limitations, visit-level data will not be released, but aggregated data are available at https://doi.org/10.7910/DVN/WANAX3. The domain scores and classifications we

used are available at https://github.com/gitronald/domains, but the NewsGuard classifications are not included because of their proprietary nature. CODE AVAILABILITY The data for this study

were collected using custom browser extensions written in JavaScript and using the WebExtension framework for cross-browser compatibility. The source code for the extensions we used in 2018

and 2020 is available at https://github.com/gitronald/webusage, and a replication package for our results is available at https://github.com/gitronald/google-exposure-engagement. The parser

we used to extract the URLs our participants were exposed to while searching is available at https://github.com/gitronald/WebSearcher. Analyses were performed with Python v.3.10.4, pandas

v.1.4.3, scipy v.1.8.1, Spark v.3.1 and R v.4.1. REFERENCES * Wagner, C. et al. Measuring algorithmically infused societies. _Nature_ 595, 197–204 (2021). Article  ADS  CAS  PubMed  Google

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and gender in psychology: five challenges to the gender binary. _Am. Psychol._ 74, 171–193 (2019). Article  PubMed  Google Scholar  Download references ACKNOWLEDGEMENTS Early versions of

this work were presented at the 2019 International Conference on Computational Social Science (IC2S2), the 2019 Conference on Politics and Computational Social Science (PaCCS) and the 2020

annual meeting of the American Political Science Association (APSA). We are grateful to the New York University Social Media and Political Participation (SMaPP) lab, the Stanford Internet

Observatory and the Stanford Social Media Lab for feedback, and to Muhammad Ahmad Bashir for development on the 2018 extension. This research was supported in part by the Democracy Fund, the

William and Flora Hewlett Foundation and the National Science Foundation (IIS-1910064). AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Stanford University, Stanford Internet Observatory,

Stanford, CA, USA Ronald E. Robertson * Northeastern University, Network Science Institute, Boston, MA, USA Ronald E. Robertson, Jon Green, Damian J. Ruck, Christo Wilson & David Lazer *

Rutgers University, School of Communication & Information, New Brunswick, NJ, USA Katherine Ognyanova * Northeastern University, Khoury College of Computer Sciences, Boston, USA Christo

Wilson Authors * Ronald E. Robertson View author publications You can also search for this author inPubMed Google Scholar * Jon Green View author publications You can also search for this

author inPubMed Google Scholar * Damian J. Ruck View author publications You can also search for this author inPubMed Google Scholar * Katherine Ognyanova View author publications You can

also search for this author inPubMed Google Scholar * Christo Wilson View author publications You can also search for this author inPubMed Google Scholar * David Lazer View author

publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS R.E.R., C.W. and D.L. conceived of the research. K.O., C.W., D.L. and R.E.R. contributed to survey

design. R.E.R. built the 2020 data collection instrument. J.G. designed the multivariate regression analysis. R.E.R. and J.G. analysed the data and R.E.R. wrote the paper with D.J.R., J.G.,

K.O., C.W. and D.L. All authors approved the final manuscript. CORRESPONDING AUTHOR Correspondence to Ronald E. Robertson. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no

competing interests. PEER REVIEW PEER REVIEW INFORMATION _Nature_ thanks Homa Hosseinmardi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer

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EXTENDED DATA FIGURES AND TABLES EXTENDED DATA FIG. 1 STRONG PARTISANS ARE EXPOSED TO SIMILAR RATES OF PARTISAN AND UNRELIABLE NEWS, BUT ASYMMETRICALLY FOLLOW AND ENGAGE WITH SUCH NEWS.

This figure complements Fig. 1 in the main text by displaying, for all 7-point PID groups, average exposure, follows and overall engagement with partisan (A, C) and unreliable news (B, D) by

study wave and 7-point PID clustered at the participant-level. Data are presented as participant-level means grouped by 7-point PID in each subplot, all error bars indicate 95% confidence

intervals (CI), and results from bivariate tests of differences in partisan and unreliable news by 7-point PID are available in Extended Data Table 2. A score of zero does not imply

neutrality in the scores we used, so left-of-zero scores do not imply a left-leaning bias (Methods, ‘Partisan News Scores’). EXTENDED DATA FIG. 2 PARTISANS WHO ENGAGE WITH MORE

IDENTITY-CONGRUENT NEWS ALSO TEND TO ENGAGE WITH MORE UNRELIABLE NEWS. This figure complements Fig. 3 in the main text by displaying all 7-point PID groups, highlighting the relationship

between partisan and unreliable news for participants’ exposure on Google Search (A, D), follows from Google Search (B, E), and overall engagement (C, F). These subplots show that the

relationship between partisan and unreliable news varies across data types, and within data types when taking partisan identity into account (Extended Data Table 3). EXTENDED DATA FIG. 3

PARTISAN NEWS DISTRIBUTIONS AT THE PARTICIPANT LEVEL FOR EACH DATASET AND STUDY WAVE. Each line represents the distribution of partisan news sources that a single participant was exposed to

in their Google Search results (A, D), followed from those results (B, E), or engaged with overall (C, F). Partisan news scores have been binned in 0.1 point intervals (e.g. −1 to −0.9, −0.9

to −0.8, etc.) along the x-axis, with tick labels showing the midpoints of those bins. SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION This file contains text that introduces several

Supplementary Information tables and provides our IRB study procedures. The Supplementary Information tables include extensive demographics for each study (Supplementary Information Tables 1

and 2), example news domains and their partisan audience bias scores (Supplementary Information Table 3), comparisons of participants’ popular search engine usage (Supplementary Information

Table 4), participant-level averages for each dataset in each study wave (Supplementary Information Table 5), the average proportion of news and unreliable news we found in each dataset and

study wave (Supplementary Information Table 6) and detailed results for each of the regressions we ran (Supplementary Information Tables 7–18). REPORTING SUMMARY PEER REVIEW FILE RIGHTS AND

PERMISSIONS Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other

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permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Robertson, R.E., Green, J., Ruck, D.J. _et al._ Users choose to engage with more partisan news than they are exposed to on Google Search.

_Nature_ 618, 342–348 (2023). https://doi.org/10.1038/s41586-023-06078-5 Download citation * Received: 17 February 2022 * Accepted: 12 April 2023 * Published: 24 May 2023 * Issue Date: 08

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