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

TL;DR

This study conducts a large-scale audit of Google search results during the 2023 Swiss Federal Elections, revealing biases in candidate representation and their predictive relationship with electoral success.

Contribution

It provides the first comprehensive analysis of gender and partisan biases in Google search results during an election campaign.

Findings

Text searches favor media sources, less so for women politicians.

Image searches reinforce stereotypes about women candidates.

Representation patterns in search results predict electoral performance.

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 patterns of candidates' representation in Google text and image searches are predictive of their electoral performance.