Auditing Partisan Audience Bias within Google Search

Citations Over TimeTop 1% of 2018 papers

Abstract

There is a growing consensus that online platforms have a systematic influence on the democratic process. However, research beyond social media is limited. In this paper, we report the results of a mixed-methods algorithm audit of partisan audience bias and personalization within Google Search. Following Donald Trump's inauguration, we recruited 187 participants to complete a survey and install a browser extension that enabled us to collect Search Engine Results Pages (SERPs) from their computers. To quantify partisan audience bias, we developed a domain-level score by leveraging the sharing propensities of registered voters on a large Twitter panel. We found little evidence for the "filter bubble'' hypothesis. Instead, we found that results positioned toward the bottom of Google SERPs were more left-leaning than results positioned toward the top, and that the direction and magnitude of overall lean varied by search query, component type (e.g. "answer boxes"), and other factors. Utilizing rank-weighted metrics that we adapted from prior work, we also found that Google's rankings shifted the average lean of SERPs to the right of their unweighted average.

Related Papers

• → Personalization Beyond Recommender Systems(2007)6 cited
• → Personalization at Scale(2021)2 cited
• Intelligent Knowledge Recommendation Methods for R&D Knowledge Portals(2004)
• Personalization of Digital Contents(2006)
• Inspirational Personalization: Abstract and Concrete Levels of Personalization(2014)