Problem
Research questions and friction points this paper is trying to address.
Hate Speech
Social Media
Political Discourse
Analyzing User Characteristics of Hate Speech Spreaders on Social Media
🤖 AI Summary
This study investigates demographic and behavioral characteristics of users who amplify hate speech on social media, focusing on heterogeneity in the dissemination of racist, sexist, and politically motivated hate content. We employ large language model–driven user clustering, interpretable machine learning (XGBoost with SHAP), and dual debiasing techniques—propensity score weighting and control function estimation—to address selection bias and endogeneity. Our analysis reveals, for the first time, a strong association between social influence (e.g., follower count, account age) and hate type: low-influence users disproportionately propagate racist and sexist content, whereas high-influence users dominate the diffusion of political hate. We further propose a bias-corrected framework and a latent vulnerability variable model, significantly improving high-risk user identification accuracy. These findings provide empirical grounding and methodological support for typology-aware, precision-targeted interventions against online hate speech.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Social Media Hate Speech
Explainable Model
User Characteristics Analysis
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