From detection to diagnosis: Discursive preclusion and the infrastructural politics of visibility on Chinese platforms

This study investigates how algorithmic infrastructures on Chinese social media govern visibility not through post-hoc deletion but through pre-emptive design. Focusing on Xiaohongshu during the 2025 China—U.S. trade conflict, it constructs a three-part comparative corpus—over 30,000 political comments from Xiaohongshu, 5000 non-political comments from the same platform, and 1200 Weibo comments on the same issue. A multi-stage diagnostic framework integrates weakly supervised anomaly detection and clustering, behavioral—semantic network analysis, topic modeling (Latent Dirichlet Allocation, LDA), and sentiment analysis to trace how expressive diversity is filtered across platforms. The results reveal no evidence of coordinated automation or bot activity but demonstrate a pronounced convergence in tone and theme: Xiaohongshu's political discourse is affectively positive, rhetorically moderate, and thematically compressed. In contrast, its non-political baseline displays greater affective heterogeneity, while Weibo's corpus shows a wider distribution of topics and discursive framings despite a similarly positive sentiment bias. These contrasts indicate that discursive alignment on Xiaohongshu arises not from manipulation or censorship, but from infrastructural filtration—ranking algorithms, participation thresholds, and affective heuristics that quietly define what can surface. The study conceptualizes this anticipatory logic as discursive preclusion, a form of algorithmic governance that renders dissent statistically improbable rather than overtly suppressed. Methodologically, it advances a “detection-to-diagnosis” approach that interprets silence and convergence as evidence of design-based control; conceptually, it reframes platform power as the governance of legibility and affect rather than of speech itself.