Biased Representation of Politicians in Google and Wikipedia Search? The Joint Effect of Party Identity, Gender Identity and Elections

ABSTRACT

Web search engines have become an important and trusted source when people seek political information. Even though previous research suggests that information about politicians in traditional and new media can provide content that makes stereotypes based on gender and party, little is known about the presence of such bias in search engines, which function as information gatekeepers in the digital age. Using quantitative text analysis and human coding techniques on a novel data set of members of the German parliament, this study examines whether search engine suggestions, i.e. search predictions, for politicians differ with respect to personal and role-oriented information based on the gender and party of the politician. It also explores whether the search engine representation of politicians changes around elections. The study further compares gender and party differences in search engine results with corresponding Wikipedia articles of the same politicians, as users are most often redirected to Wikipedia from Google. The results suggest that politicians’ representation in search engines and Wikipedia are structured by a joint effect of their gender and party identity. While Google suggestions provide less personal information about female politicians belonging to a right-wing party compared to their male counterparts, this relationship is not observable for left-wing parties. Moreover, there are changes in gender biases around the election. In Wikipedia articles, politicians belonging to right-wing parties are represented with more personal information compared to politicians belonging to left ones, an effect which is even stronger for females.

KEYWORDS:

• Search engines
• gender bias
• partisan bias
• Google
• Wikipedia

Acknowledgement

This research was supported by the Digital Society research program funded by the Ministry of Culture and Science of the German State of North Rhine-Westphalia. I would also like to thank Sven-Oliver Proksch, Bruno Castanho Silva, members of the Digital Society research program, participants of the CCCP Research Seminar and the Conference of the European Political Science Association 2019 for their very helpful comments on a previous version of the paper. I am also grateful for support and useful comments related to the data access and information retrieval by Philipp Schaer and Malte Bonart. I thank the two anonymous referees whose constructive comments helped to improve this manuscript, as well as Danielle Pullan for language editing.

Disclosure Statement

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Notes

1. The data has been obtained from the website https://gs.statcounter.com/search-engine-market-share (February 4, 2020).

2. The data has been obtained from the website https://www.alexa.com/siteinfo/google.com (February 4, 2020).

3. This information is provided by Wikipedia on the website https://en.wikipedia.org/wiki/Wikipedia (February 4, 2020).

4. The data has been obtained from the website https://www.alexa.com/siteinfo/wikipedia.org (February 4, 2020).

5. Related work has been mainly focused on systematic gender differences in other online platforms such as biases in the representation on Wikipedia (Claudia Wagner et al., 2015; Klein et al., 2016; Zagovora et al., 2017) and biases in the communication on Twitter (Evans, 2016; Meeks, 2016; Mertens et al., 2019).

6. The data has been obtained from the website https://www.bundestag.de/abgeordnete/biografien/mdb_zahlen_19/frauen_maenner-529508 (February 11, 2020).

7. Replication materials are available on the website https://github.com/fpradel/biased_representation_of_politicians.

8. The data has been collected with the API http://clients1.google.de/complete/search.

9. The data set can be requested on the website https://www.bundeswahlleiter.de/en/.

10. The data set is has been obtained from the website http://everypolitician.org/germany/.

Additional information

Notes on contributors

Franziska Pradel

Franziska Pradel is a doctoral researcher at the Cologne Center for Comparative Politics at the University of Cologne (Germany) and member of the state-wide research program Digital Society. Her research interests focus on biases in search engines and online platforms, digitalization, political communication and the link between social identity and political behavior.