Quantifying public opinion evolution and information behavior dynamics: a framework coupling event knowledge graphs with centroid-aligned topic modeling

Public health emergency monitoring on social media is crucial for proactive risk mitigation. However, this process remains constrained by short-text semantic noise and the fragmented integration of discourse structures and behavioral heterogeneity. To bridge this gap, this paper introduces a dual-level framework integrating Event Knowledge Graphs (EKG) and user behavior profiling, built on a Centroid-Alignment Factor (C-AF) embedding-enhanced BERTopic model. Applied to 22,744 Weibo posts related to the Chikungunya fever outbreak, the C-AF model optimizes Topic Diversity (TD) from 0.5725 to 0.6480, enabling the construction of a high-precision EKG that reveals core discourse evolutionary pathways. For user behavior segmentation, the framework achieves a peak Silhouette Coefficient of 0.909 (outperforming the 0.670 baseline by 35.7%), further validated by an external semantic indicator (TC improving from 0.550 to 0.783). This dual-validation successfully identifies five distinct user profiles: Early Cognitive, Knowledge Dissemination, Risk-Attention, Prevention-Focused, and Preventive Alert types. This framework offers a scalable, data-driven tool for precise public health crisis monitoring and targeted digital governance intervention.