From Public Discourse to Affective Tribes: Mechanisms of Affective Communication and Moral Judgment in Social Media Gender Debates

Abstract

In recent years, social media has fundamentally transformed the landscape of public discourse on gender issues. Instead of facilitating rational deliberation within a traditional public sphere, digital platforms have devolved into polarized battlegrounds characterized by affective tribalism. This study investigates the mechanisms driving the shift from rational public opinion to emotional side-taking and aggressive moral policing in online gender debates. Utilizing the highly polarized "Yang Li and JD.com" controversy as a primary case study, this research employs a mixed-methods approach, integrating computational sentiment analysis (NLP) and qualitative discourse analysis, to examine 42,000 highly engaged Weibo comments. The empirical findings reveal three key phenomena. First, sentiment analysis demonstrates a severe U-shaped (bimodal) distribution, providing quantitative evidence for the hollowing-out of the objective middle ground and the dominance of extreme emotions. Second, lexical mapping illustrates that users increasingly bypass logical argumentation, deploying stigmatizing gender labels as heuristic tools for tribal boundary-drawing. Third, statistical correlation reveals that content with high emotional volatility and absolute moral framing receives significantly higher user engagement. Theoretically, this paper argues that affective communication functions as a mechanism for identity politics, where expressing collective outrage confirms tribal loyalty and escalates minor frictions into systemic moral judgments. Furthermore, the study highlights the structural complicity of platform algorithms, which systematically reward polarized outrage to maximize the attention economy. To reconstruct a deliberative digital space, this paper calls for algorithmic accountability and the cultivation of affective digital literacy.