Pu ZHANG (张谱)

As extreme heatwaves intensify into a primary public health threat under climate change, understanding the dynamics of public perception is critical for effective risk communication. While traditional methods are limited in scale and timeliness, social media offers an unprecedented real-time window into public discourse. This study provides the first large-scale, computational analysis of Chinese public discourse on extreme heat, leveraging 348,417 user comments from the platform Douyin. After addressing multicollinearity through a rigorous variable reduction process, our novel framework combines a Large Language Model (LLM) for thematic classification with an FDR-corrected regression model and an explainable AI (SHAP) analysis to identify and interpret the drivers of public concern. Thematically, we find a stark divergence between the most frequent topics (e.g., regional comparisons) and the most publicly endorsed topics (e.g., fatalism and mutual care), revealing latent community values. Our regression analysis, corrected for multiple comparisons, reveals a powerful and statistically robust link: higher maximum temperatures directly drive increased public discourse on both “Health Impacts” and “Entertainment and Emotional Venting.” The SHAP analysis provides a deeper layer of insight, revealing that underlying geographical context (i.e., average elevation) and socioeconomic status exert the greatest overall influence on the nature of the conversation, shaping everything from tourism-related discussions to expressions of social vulnerability. These findings demonstrate that public discourse on heat is shaped by a hierarchy of factors, from statistically robust climate triggers to highly impactful socioeconomic and geographical contexts. This work provides a scalable framework for a dynamic Social and Health Impact Assessment (SIA/HIA), underscoring the need for tailored communication strategies that account for both environmental exposure and social vulnerability.

Sorting out the changing characteristics of online public opinion triggered by a series of events in the investigation and assessment of major natural disasters is of great practical significance for optimizing the work of disaster investigation and assessment, governing the ecology of online public opinion, and enhancing the effect of comprehensive disaster reduction. In this paper, we collected relevant comments from several official media accounts, such as People’s Daily, and evaluated their emotional color using a sentiment analysis method based on the BERT fine-tuning model. Furthermore, keyword co-occurrence semantic network theme analysis is conducted for texts presenting negative emotional overtones to assess the changes in public opinion hotspots. The impact of the relevant online public opinion characteristics and the release of the disaster investigation report on them was assessed in the context of the investigation report itself. Based on sorting out the characteristics of online public opinion on several related topics, targeted public opinion governance initiatives and relevant suggestions for improving the disaster investigation system are proposed. This paper is of positive significance for studying disaster public opinion and improving the effectiveness of emergency management of rainstorms and flooding disasters.