Engagement Outweighs Exposure to Partisan and Unreliable News within Google Search

If popular online platforms systematically expose their users to partisan and unreliable news, they could potentially contribute to societal issues like rising political polarization. This concern is central to the “echo chamber” and “filter bubble” debates, which critique the roles that user choice and algorithmic curation play in guiding users to different online information sources. These roles can be measured in terms of exposure, defined as the URLs seen while using an online platform, and engagement, defined as the URLs selected while on that platform or browsing the web more generally. However, due to the challenges of obtaining ecologically valid exposure data—what real users saw during their regular platform use—studies in this vein often only examine engagement data, or estimate exposure via simulated behavior or inference. Despite their centrality to the contemporary information ecosystem, few such studies have focused on web search, and even fewer have examined both exposure and engagement on any platform. 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 U.S. elections. In both waves, we found that participants’ partisan identification had a small and inconsistent relationship with the amount of partisan and unreliable news they were exposed to on Google Search, a more consistent relationship with the search results they chose to follow, and the most consistent relationship with their overall engagement. That is, compared to the news sources our participants were exposed to on Google Search, we found more identity-congruent and unreliable news sources in their engagement choices, both within Google Search and overall. These results suggest that exposure and engagement with partisan or unreliable news on Google Search are not primarily driven by algorithmic curation, but by users’ own choices. 1 ar X iv :2 20 1. 00 07 4v 3 [ cs . S I] 2 8 Se p 20 22 The prevalence of partisan and unreliable online news is a topic of ongoing concern among policymakers, civil society organizations, and academics [1–4]. These concerns often center the role of online platforms, such as search engines or social media, in directing people to such content, and the societal impacts that such guiding may incur [5, 6]. The theoretical grounding for such concerns generally involves selective exposure—the tendency to choose political information that is congruent with one’s existing beliefs [7–9]—and its counterparts: echo chambers [10, 11] and filter bubbles [12, 13]. In online settings, echo chambers often center the role of users’ choices, including direct navigation, search query formulation, or social networking decisions, and how they can create settings “in which most available information conforms to pre-existing attitudes and biases” [4]. In contrast, filter bubbles center the role of algorithmic curation, such as the production of a social media feed or a search results page, where content “selected by algorithms according to a viewer’s previous behaviors” can create a feedback loop that limits exposure to cross-cutting content [13]. Although definitions for these concepts vary and overlap, both can be described in terms of user choice and algorithmic curation [14, 15], and both predict a similar observable outcome: partisans will see and select a significant amount of identity-congruent content. Recent research on partisan and unreliable online news has primarily focused on users’ choices on social media platforms [3, 11, 16–18] or during general web browsing [14, 19–22], leaving open questions about algorithmic curation, especially on web search engines. The importance of studying news in web search is evident from long-standing concerns about the impact of search engines [23–26], and urgent in light of several recent findings. For example, survey and digital trace studies have found that web search plays a central role in directing users to online news [19, 27–31], qualitative work has documented patterns of unreliable and false information in web search results [32, 33], and lab experiments suggest politically-biased search rankings can influence political opinions [34, 35]. However, research on news seen within web search has generally been limited to algorithm auditing studies—where what real users might have seen in their search results was estimated using simulated user behavior (hypothetical exposure) [36–41]—and digital trace studies—where what real users might have seen was estimated from available click logs (selected exposure) [14, 15, 19, 42, 43]. We operationalize the two sides of a user-platform interaction as exposure and engagement, defining exposure as the URLs people see while visiting a platform and engagement as the URLs that people interact with while on that platform or while browsing the web more broadly. We also define follows as the intersection of these data types—engagement conditional on exposure—representing instances in which a person engages with a URL immediately after