Temporal and Spatial Analysis of Public Emotion on Social Media During Earthquake Disaster—A Case Study of Jishishan Earthquake in 2023

This study explores the spatiotemporal characteristics of social media users' emotional responses to natural disasters, providing valuable insights for government agencies to guide public sentiment, enhance emergency responses, and facilitate post-disaster reconstruction. It uses the 6.2 magnitude earthquake in Jishishan, Gansu, as a case study, collecting Weibo postings, and applying Snow NLP for sentiment analysis, and using DUTIR method for sentiment classification. This study examines the dynamics of public emotional expression over time and their spatial distribution during the disaster. Key findings indicate that the volume of social media posts about the Jishishan earthquake has shown a fluctuating downward trend, predominantly characterized by positive emotional expressions. The posting volume and the nature of emotional expression are influenced by various factors, including economic and social conditions, the progress of rescue efforts, the frequency of disasters, the extent of impact experienced by those affected, personal experiences of the disaster, and collective memory of the disaster, all exhibiting temporal and regional variations. The spatial distribution of these emotional expressions showed a negative correlation with the severity of the disaster impact, although this pattern was not evident in the epicenter region. Areas with memories of past disasters exhibited a higher prevalence of "sadness", regions more severely affected by the disaster displayed a greater proportion of "disgust", and the epicenter region had a higher volume of posts expressing "fear". As a case study, this research provides insights for decision-makers and the government to better understand public sentiments during disasters.