Professor in Political Science and Computer and Information Science

David Lazer

Auditing Partisan Audience Bias within Google Search

Peer Reviewed Computer Science Conference
Publication date: 
01/2019
Authors: 
Ronald Robertson
Shan Jiang
Kenneth Joseph
Lisa Friedland
David Lazer
Christo Wilson
Auditing Partisan Audience Bias within Google Search

There is a growing concensus 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 towards 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.

Research Areas TOC

Computational Social Science, Collective Cognition

Computational Social Science, 21st Century Democracy, Political Networks, Collective Cognition, Networked Governance, Regulation, DNA and the Criminal Justice System

Computational Social Science, 21st Century Democracy, Political Networks

DNA and the Criminal Justice System