Polarized Patterns of Language Toxicity and Sentiment of Debunking Posts on Social Media

2025-01-10

AI Summary

This study investigates how linguistic aggressiveness, emotional polarization, and platform-specific mechanisms affect conversational health in social media rumor correction. Analyzing 86 million Twitter and 4 million Reddit rumor-correction posts, we integrate large-scale text mining, cross-platform comparative analysis, joint toxicity–sentiment modeling, and network topology analysis. We first identify that peripheral users (one-degree connections) predominantly generate toxic discourse. Second, we reveal platform architectural differences: Twitter amplifies partisan polarization, whereas Reddit increases overall toxicity but mitigates pessimistic sentiment. Third, we empirically confirm a significant negative correlation between toxicity and pessimism, and demonstrate that increased interaction frequency effectively reduces toxicity—particularly on Reddit. These findings provide empirical evidence and mechanistic insights for platform governance and the design of healthier, more effective rumor-correction interventions.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Here's a condensed 1920-character version: The rise of misinformation and fake news in online political discourse poses significant challenges to democratic processes and public engagement. While debunking efforts aim to counteract misinformation and foster fact-based dialogue, these discussions often involve language toxicity and emotional polarization. We examined over 86 million debunking tweets and more than 4 million Reddit debunking comments to investigate the relationship between language toxicity, pessimism, and social polarization in debunking efforts. Focusing on discussions of the 2016 and 2020 U.S. presidential elections and the QAnon conspiracy theory, our analysis reveals three key findings: (1) peripheral participants (1-degree users) play a disproportionate role in shaping toxic discourse, driven by lower community accountability and emotional expression; (2) platform mechanisms significantly influence polarization, with Twitter amplifying partisan differences and Reddit fostering higher overall toxicity due to its structured, community-driven interactions; and (3) a negative correlation exists between language toxicity and pessimism, with increased interaction reducing toxicity, especially on Reddit. We show that platform architecture affects informational complexity of user interactions, with Twitter promoting concentrated, uniform discourse and Reddit encouraging diverse, complex communication. Our findings highlight the importance of user engagement patterns, platform dynamics, and emotional expressions in shaping polarization in debunking discourse. This study offers insights for policymakers and platform designers to mitigate harmful effects and promote healthier online discussions, with implications for understanding misinformation, hate speech, and political polarization in digital environments.

Problem

• Social Media
• Misinformation
• Communication Health

Innovation

• Social Media Analysis
• Harmful Language in Fact-checking
• Platform Design for Healthy Discourse