Backfire Effect Reveals Early Controversy in Online Media

2024-03-05

arXiv.org

AI Summary

This paper addresses the ultra-early detection of controversy in online media, specifically focusing on the cognitive backfire effect triggered by ideological conflict. Method: Drawing from psychological theory, we formalize the backfire effect as two computable gradient features—“like escalation gradient” and “reply escalation gradient”—and integrate them into a few-shot, highly interpretable controversy detection framework. The framework jointly models multi-source interaction structures and employs cross-lingual (Chinese/English) comparative evaluation to enhance robustness and generalizability. Contribution/Results: Experiments across multiple platforms demonstrate that the proposed gradient features significantly outperform conventional textual and structural baselines in early controversy identification, achieving marked improvements in accuracy. The framework exhibits strong cross-platform generalizability and provides mechanistic interpretability grounded in cognitive science, establishing a novel paradigm for modeling controversy evolution.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Application Domains: Humanities & Computational Social Science

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization

Abstract

The rapid development of online media has significantly facilitated the public's information consumption, knowledge acquisition, and opinion exchange. However, it has also led to more violent conflicts in online discussions. Therefore, controversy detection becomes important for computational and social sciences. Previous research on detection methods has primarily focused on larger datasets and more complex computational models but has rarely examined the underlying mechanisms of conflict, particularly the psychological motivations behind them. In this paper, we present evidence that conflicting posts tend to have a high proportion of"ascending gradient of likes", i.e., replies get more likes than comments. Additionally, there is a gradient in the number of replies between the neighboring tiers as well. We develop two new gradient features and demonstrate the common enhancement effect of our features in terms of controversy detection models. Further, multiple evaluation algorithms are used to compare structural, interactive, and textual features with the new features across multiple Chinese and English media. The results show that it is a general case that gradient features are significantly different in terms of controversy and are more important than other features. More thoroughly, we discuss the mechanism by which the ascending gradient emerges, suggesting that the case is related to the"backfire effect"in ideological conflicts that have received recent attention. The features formed by the psychological mechanism also show excellent detection performance in application scenarios where only a few hot information or early information are considered. Our findings can provide a new perspective for online conflict behavior analysis and early detection.

Problem

• Detect online controversy using gradient features in likes and replies
• Explore psychological mechanisms like backfire effect in ideological conflicts
• Improve early detection of conflicts with limited hot or early data

Innovation

• Uses ascending gradient of likes feature
• Develops new gradient features for detection
• Links gradient features to backfire effect