Backfire Effect Reveals Early Controversy in Online Media

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 propose a lightweight and language-independent method for controversy detection by introducing two novel psychological features: ascending gradient (AG) and tier ascending gradient (TAG). These features capture psychological signals in user interactions—specifically, the patterns where controversial comments generate disproportionate replies or replies outperform parent comments in likes. We develop these features based on the theory of the backfire effect in ideological conflict and demonstrate their consistent effectiveness across models and platforms. Compared with structural, interaction, and text-based features, AG and TAG show higher importance scores and better generalizability. Extensive experiments on Chinese and English platforms (Reddit, Toutiao, and Sina) confirm the robustness of our features across languages and algorithms. Moreover, the features exhibit strong performance even when applied to early-stage data or limited “one-page” scenarios, supporting their utility for early controversy detection. Our work highlights a new psychological perspective on conflict behavior in online discussions and bridges behavioral patterns and computational modeling.