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Users choose to engage with more partisan news than they are exposed to on Google Search

Nature volume 618, pages 342–348 (2023)Cite this article

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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 on engagement data4,8,11,12,13,14,15,16 or estimates of hypothetical exposure17,18,19,20,21,22,23. Studies involving ecological exposure have therefore been rare, and largely limited to social media platforms7,24, 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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Fig. 1: Measuring user choice and algorithmic curation as engagement and exposure.
Fig. 2: Strong partisans are exposed to similar rates of partisan and unreliable news, but asymmetrically follow and engage with such news.
Fig. 3: Exposure to unreliable news is less concentrated among a small number of participants than follows or overall engagement.
Fig. 4: Partisans who engage with more identity-congruent news also tend to engage with more unreliable news.
Fig. 5: Differences in strong partisans’ exposure to partisan news were not significant when controlling for demographics and search queries.

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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.

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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).

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Authors and Affiliations

  1. Stanford University, Stanford Internet Observatory, Stanford, CA, USA

    Ronald E. Robertson

  2. Northeastern University, Network Science Institute, Boston, MA, USA

    Ronald E. Robertson, Jon Green, Damian J. Ruck, Christo Wilson & David Lazer

  3. Rutgers University, School of Communication & Information, New Brunswick, NJ, USA

    Katherine Ognyanova

  4. Northeastern University, Khoury College of Computer Sciences, Boston, USA

    Christo Wilson

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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.

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Correspondence to Ronald E. Robertson.

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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.

Extended Data Table 1 Descriptive counts for each exposure, follows, and overall engagement dataset
Full size table
Extended Data Table 2 Kruskal-Wallis H tests by study wave, data type, data source, and user grouping
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Extended Data Table 3 Spearman’s ρ rank correlations comparing average news partisanship and proportion of unreliable news
Full size table
Extended Data Table 4 Main multivariate regression results for partisan news in 2018
Full size table
Extended Data Table 5 Main multivariate regression results for partisan news in 2020
Full size table
Extended Data Table 6 Main multivariate regression results for unreliable news in 2018
Full size table
Extended Data Table 7 Main multivariate regression results for unreliable news in 2018
Full size table

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).

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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

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