Hate Speech in the Political Discourse on Social Media: Disparities Across Parties, Gender, and Ethnicity
Kirill Solovev, Nicolas Pr\"ollochs

TL;DR
This study empirically examines how hate speech in Twitter replies to U.S. Congress members varies based on politicians' party, gender, and ethnicity, revealing significant disparities influenced by personal characteristics and sentiment.
Contribution
It provides a novel empirical analysis of hate speech disparities across political, gender, and ethnic lines using machine learning and hierarchical regression models.
Findings
Hate speech is more prevalent in replies to persons of color and women.
Negative sentiment in source tweets correlates with increased hate speech in replies.
Disparities in hate speech depend on party affiliation, with different patterns for Democrats and Republicans.
Abstract
Social media has become an indispensable channel for political communication. However, the political discourse is increasingly characterized by hate speech, which affects not only the reputation of individual politicians but also the functioning of society at large. In this work, we empirically analyze how the amount of hate speech in replies to posts from politicians on Twitter depends on personal characteristics, such as their party affiliation, gender, and ethnicity. For this purpose, we employ Twitter's Historical API to collect every tweet posted by members of the 117th U.S. Congress for an observation period of more than six months. Additionally, we gather replies for each tweet and use machine learning to predict the amount of hate speech they embed. Subsequently, we implement hierarchical regression models to analyze whether politicians with certain characteristics receive more…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
