Weibo public opinion analysis of emergencies using lexicon-based and deep learning approaches: a case study of the 12.18 Jishishan Seismic Event

Le résumé fourni par la source

Seismic events, as sudden natural disasters, significantly impact society and the economy. Analyzing post-disaster online public opinion helps quickly assess the situation, severity, and public needs, aiding sentiment management and emergency response. This study collected Sina Weibo public opinion data within 24 h after the Ms6.2 earthquake that struck Jishishan County, Gansu Province, on December 18, 2023. The distribution characteristics of public opinion and micro-charity and the correlation between public attention and micro-charity participation were analyzed. Sentiment analysis was further conducted using an improved sentiment lexicon-based approach and the Text-CNN method. The results indicate that the public reactions were most intense within the first hour after the earthquake, followed by three subsequent fluctuations. In terms of sentiment, positive-sentiment blog posts were the most common. Regarding micro-charity, users in regions highly concerned about the ‘#Gansu Earthquake’ topic were more involved. Different from previous studies, this study reveals a positive correlation between media exposure and micro-charity, suggesting that provinces with greater emergency attention are more active in micro-charity. The findings of this study are significant for disaster emergency management and online public opinion analysis in the new era.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.