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Campaigning through the lens of Google: A large-scale algorithm audit of Google searches in the run-up to the Swiss Federal Elections 2023

Elections
Elites
Gender
Campaign
Candidate
Internet
Big Data
Empirical
Tobias Rohrbach
University of Fribourg
Mykola Makhortykh
Universität Bern
Tobias Rohrbach
University of Fribourg
Maryna Sydorova
Universität Bern

Abstract

Search engines like Google have become major sources of information for voters during election campaigns. To assess potential biases across candidates’ gender and partisan identities in the algorithmic curation of candidate information, we conducted a large-scale algorithm audit analyzing Google’s selection and ranking of information about candidates for the 2023 Swiss Federal Elections, three and one week before the election day. Results indicate that text searches prioritize media sources in search output but less so for women politicians. Image searches revealed a tendency to reinforce stereotypes about women candidates, marked by a disproportionate focus on stereotypically pleasant emotions for women, particularly among right-leaning candidates. Crucially, we find that gendered patterns of candidates’ representation in Google text and image searches are predictive of their electoral performance in ways that hinder women's advances into positions of political decision-making.