Analyzing User Characteristics of Hate Speech Spreaders on Social Media

TL;DR

This study investigates user characteristics influencing hate speech resharing on social media, revealing that users with less social influence tend to share more hate speech, with variations across hate speech types.

Contribution

The paper introduces a novel analysis of user attributes and their impact on hate speech resharing, using large language models and explainable machine learning with debiasing techniques.

Findings

Users with fewer followers and posts share more hate speech.

Heterogeneity exists across hate speech types, with some spread by less influential users.

Understanding these factors aids in designing better mitigation strategies.

Abstract

Hate speech on social media threatens the mental and physical well-being of individuals and contributes to real-world violence. Resharing is an important driver behind the spread of hate speech on social media. Yet, little is known about who reshares hate speech and what their characteristics are. In this paper, we analyze the role of user characteristics in hate speech resharing across different types of hate speech (e.g., political hate). For this, we proceed as follows: First, we cluster hate speech posts using large language models to identify different types of hate speech. Then we model the effects of user attributes on users' probability to reshare hate speech using an explainable machine learning model. To do so, we apply debiasing to control for selection bias in our observational social media data and further control for the latent vulnerability of users to hate speech. We find that, all else equal, users with fewer followers, fewer friends, fewer posts, and older accounts share more hate speech. This shows that users with little social influence tend to share more hate speech. Further, we find substantial heterogeneity across different types of hate speech. For example, racist and misogynistic hate is spread mostly by users with little social influence. In contrast, political anti-Trump and anti-right-wing hate is reshared by users with larger social influence. Overall, understanding the factors that drive users to share hate speech is crucial for detecting individuals at risk of engaging in harmful behavior and for designing effective mitigation strategies.