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If popular online platforms systematically expose their users to partisan andunreliable news, they could potentially contribute to societal issues likerising political polarization. This concern is central to the echo chamber andfilter bubble debates, which critique the roles that user choice andalgorithmic curation play in guiding users to different online informationsources. These roles can be measured in terms of exposure, the URLs seen whileusing an online platform, and engagement, the URLs selected while on thatplatform or browsing the web more generally. However, due to the challenges ofobtaining ecologically valid exposure data--what real users saw during theirregular platform use--studies in this vein often only examine engagement data,or estimate exposure via simulated behavior or inference. Despite theircentrality to the contemporary information ecosystem, few such studies havefocused on web search, and even fewer have examined both exposure andengagement on any platform. To address these gaps, we conducted a two-wavestudy pairing surveys with ecologically valid measures of exposure andengagement on Google Search during the 2018 and 2020 US elections. We foundthat participants' partisan identification had a small and inconsistentrelationship with the amount of partisan and unreliable news they were exposedto on Google Search, a more consistent relationship with the search resultsthey chose to follow, and the most consistent relationship with their overallengagement. That is, compared to the news sources our participants were exposedto on Google Search, we found more identity-congruent and unreliable newssources in their engagement choices, both within Google Search and overall.These results suggest that exposure and engagement with partisan or unreliablenews on Google Search are not primarily driven by algorithmic curation, but byusers' own choices.

Engagement Outweighs Exposure to Partisan and Unreliable News within Google Search