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

Auditing Partisan Audience Bias within Google Search. Ronald E. Robertson et al. Proceedings of the ACM on Human-Computer Interaction - CSCW archive. Volume 2 Issue CSCW, November 2018, Article No. 148, doi: 10.1145/3274417

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.


Check also: Few people are actually trapped in filter bubbles. Why do they like to say that they are? Plus: Are your Google results really that different from your neighbor’s? Laura Hazard Owen. NiemanLab, Dec 07 2018. http://www.niemanlab.org/2018/12/few-people-are-actually-trapped-in-filter-bubbles-why-do-they-like-to-say-that-they-are