  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, the URLs seen while
using an online platform, and engagement, 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 exposure and
engagement on Google Search during the 2018 and 2020 US elections. 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.
