research-article

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Auditing Partisan Audience Bias within Google Search

Authors:

Ronald E. Robertson,

Kenneth Joseph,

Lisa Friedland,

Christo WilsonAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 2, Issue CSCW

Article No.: 148, Pages 1 - 22

https://doi.org/10.1145/3274417

Published: 01 November 2018 Publication History

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.

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 2, Issue CSCW

November 2018

4104 pages

EISSN:2573-0142

DOI:10.1145/3290265

Editors:
Karrie Karahalios
University of Illinois & Adobe
,
Andrés Monroy-Hernández
Snap Inc.
,
Airi Lampinen
Stockholm University
,
Geraldine Fitzpatrick
Vienna University of Technology

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https://dl.acm.org/doi/10.1145/3689372
Wang SHuang SZhou AMetaxa D(2024)Lower Quantity, Higher Quality: Auditing News Content and User Perceptions on Twitter/X Algorithmic versus Chronological TimelinesProceedings of the ACM on Human-Computer Interaction10.1145/36870468:CSCW2(1-25)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687046
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