Identifying Constructive Conflict in Online Discussions through Controversial yet Toxicity Resilient Posts

2025-09-22

AI Summary

This study addresses the “echo chamber” phenomenon and harmful interactions on social media by introducing the concept of *constructive conflict*—identifying high-quality political posts that are simultaneously controversial (eliciting diverse viewpoints) and toxicity-resistant (withstanding malicious responses). Method: We propose a dual-channel machine learning model that jointly models controversy and toxicity resistance, integrating linguistic features (e.g., gratitude expressions, hedging, politeness strategies) with response behavioral patterns. Contribution/Results: Empirical analysis reveals that certain highly controversial political posts significantly suppress toxic replies, demonstrating the efficacy of tone modulation in elevating discourse quality. Our model accurately distinguishes low-toxicity posts from genuinely toxicity-resistant ones. To our knowledge, this is the first work to systematically decouple controversy from toxicity, offering a computationally grounded theoretical framework and practical tools for fostering rational public deliberation.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Machine Learning: Multimodal Learning

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
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Bridging content that brings together individuals with opposing viewpoints on social media remains elusive, overshadowed by echo chambers and toxic exchanges. We propose that algorithmic curation could surface such content by considering constructive conflicts as a foundational criterion. We operationalize this criterion through controversiality to identify challenging dialogues and toxicity resilience to capture respectful conversations. We develop high-accuracy models to capture these dimensions. Analyses based on these models demonstrate that assessing resilience to toxic responses is not the same as identifying low-toxicity posts. We also find that political posts are often controversial and tend to attract more toxic responses. However, some posts, even the political ones, are resilient to toxicity despite being highly controversial, potentially sparking civil engagement. Toxicity resilient posts tend to use politeness cues, such as showing gratitude and hedging. These findings suggest the potential for framing the tone of posts to encourage constructive political discussions.

Problem

• Identifying constructive conflict in online discussions through controversial but respectful posts
• Developing models to distinguish toxicity-resilient content from low-toxicity posts
• Framing post tone to encourage civil political engagement despite controversy

Innovation

• Algorithmic curation using constructive conflict criteria
• High-accuracy models for controversiality and toxicity resilience
• Identifying posts with politeness cues for civil engagement