Beyond the Social Media Contents: The Role of Social Interactions in Stance Detection

Abstract

Stance detection categorizes the stance toward a specific target or topic, or other aspects of interest (e.g., a social media comment) as favor, against, or neutral. The discourse of stance detection has evolved from court debate to social media, particularly in the analyses of political, social, and health issues. Despite its long-standing history, stance detection still faces significant challenges partly due to ambiguous, diverse, and informal expressions of human language in social media. Motivated by the affordance of social interactions on social media platforms, this study aims to investigate whether social interactions are useful and how to represent and incorporate them into stance detection models effectively. To this end, we propose a framework that integrates graph learning with transformers. The empirical evaluation results with an extended benchmark dataset in the political discourse demonstrate the superior performance of the framework to the state-of-the-art baseline models and highlight the significant role of social interaction networks in stance detection. The framework can also be used to guide the efforts in social media monitoring, marketing, and informed decision-making.