Mapping Election Toxicity on Social Media across Issue, Ideology, and Psychosocial Dimensions

2026-04-17

AI Summary

This study investigates the multidimensional dynamics of political discourse toxicity on X during the 2024 U.S. presidential election, focusing on the interplay among issue domains, ideological orientation, and psychosocial factors. Integrating human-in-the-loop LLM-assisted annotation, large language model–based toxicity detection, psycholinguistic analysis, and moral foundations theory, the research presents the first unified modeling of these dimensions, uncovering the contextual dependency of toxic expression and a cross-partisan emotional mirroring effect. Findings reveal that identity-related topics exhibit the highest toxicity and concentrated hate speech, with pervasive harassment behaviors; furthermore, left- and right-leaning users display similar affective profiles on identical issues, and their deployment of moral foundations is significantly moderated by issue type. The results underscore the necessity of issue-sensitive strategies in toxicity mitigation.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization

Abstract

Online political hostility is pervasive, yet it remains unclear how toxicity varies across campaign issues and political ideology, and what psychosocial signals and framing accompany toxic expression online. In this work, we present a large-scale analysis of discourse on X (Twitter) during the five weeks surrounding the 2024 U.S. presidential election. We categorize posts into 10 major campaign issues, estimate the ideology of posts using a human-in-the-loop LLM-assisted annotation process, detect harmful content with an LLM-based toxicity detection model, and then examine the psychological drivers of toxic content. We use these annotated data to examine how harmful content varies across campaign issues and ideologies, as well as how emotional tone and moral framing shape toxicity in election discussions. Our results show issue heterogeneity in both the prevalence and intensity of toxicity. Identity-related issues displayed the highest toxicity intensity. As for specific harm categories, harassment was most prevalent and intense across most of the issues, while hate concentrated in identity-centered debates. Partisan posts contained more harmful content than neutral posts, and ideological asymmetries in toxicity varied by issue. In terms of psycholinguistic dimensions, we found that toxic discourse is dominated by high-arousal negative emotions. Left- and right-leaning posts often exhibit similar emotional profiles within the same issue domain, suggesting emotional mirroring. Partisan groups frequently rely on overlapping moral foundations, while issue context strongly shapes which moral foundations become most salient. These findings provide a fine-grained account of toxic political discourse on social media and highlight that online political toxicity is highly context-dependent, underscoring the need for issue-sensitive approaches to measuring and mitigating it.

Problem

• online political toxicity
• campaign issues
• political ideology
• psychosocial signals
• social media discourse

Innovation

• LLM-assisted annotation
• toxicity detection
• ideological asymmetry
• psycholinguistic analysis
• moral framing