A Leader and a Lady? A Computational Approach to Detection of Political Gender Stereotypes in Facebook User Comments

Authors

  • Aliya Andrich Ilmenau University of Technology
  • Emese Domahidi Ilmenau University of Technology

Keywords:

gender stereotypes, politicians, social media, Facebook, computational methods, word embeddings

Abstract

Voters tend to perceive female and male politicians differently, viewing women in politics through the prism of existing gender stereotypes. Although social media have become one of the key platforms for political communication, little is known about stereotypes that social media users communicate about political candidates. This study investigates how gender influences citizens’ evaluations of more than 500 U.S. politicians on social media. Drawing on a large sample of Facebook user comments (n = 13,866,507), we find that female politicians are discussed using traits describing women’s personality and appearance. We also show that users associate female politicians with leadership, competence, and empathy. However, the results are different for highly prominent politicians. Specifically, our findings support the idea of leadership roles being more strongly linked to the masculine stereotype, as we observe that Donald Trump is more strongly associated with masculinity and traits relevant for a political career than Hillary Clinton.

Author Biographies

Aliya Andrich, Ilmenau University of Technology

(MA, Ilmenau University of Technology) is a researcher with the Computational Communication Science research group at the Ilmenau University of Technology. She is interested in studying biases in digital media content and their effects on individuals and society.

Emese Domahidi, Ilmenau University of Technology

(PhD, University of Münster) is an assistant professor and head of the Computational Communication Science research group at the Ilmenau University of Technology. Her current projects include research of biases in digital media, health communication, and social consequences of online media use.

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Published

2022-12-29

Issue

Vol. 17 (2023)

Section

Articles

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