Extracting public opinion on typhoon disasters in China: a sina weibo case study of landfalling typhoon Muifa (2022)
Yanran Sun, Qian Wang, Yongchang Zhu, Jing Xu, Lu Liu, Chunyi Xiang, Chuanhai Qian

TL;DR
This study analyzes public reactions on Sina Weibo during Typhoon Muifa (2022) to understand how social media reflects public sentiment and attention during disasters.
Contribution
The study introduces a method to track public opinion dynamics on social media during typhoons using topic modeling and sentiment analysis.
Findings
Four main topics emerged: typhoon impact, weather conditions, meteorological info, and disaster response.
Official accounts dominated discussions on weather and disaster response, while personal accounts focused on impact and conditions.
Negative sentiment correlated strongly with rising precipitation, especially in forecasted landfall provinces.
Abstract
This study investigates public opinion dynamics on Sina Weibo during Typhoon Muifa (2022), which made four landfalls in China. Using a dataset of 19,417 microblog posts, we employed Latent Dirichlet Allocation (LDA) topic modeling, sentiment analysis, and correlation statistics to characterize the evolution of public attention and discourse alongside the typhoon’s activity. Results identified four dominant discussion topic categories: typhoon impact, weather conditions, meteorological information, and disaster response. Personal accounts predominantly contributed to the first two categories, while official accounts dominated discussions on the latter two. A strong positive correlation emerged between daily total precipitation and the number of microblog counts (R2 = 0.84, q < 0.001), which was particularly pronounced in forecasted landfall provinces Zhejiang, Shanghai, Shandong, and…
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Taxonomy
TopicsPublic Relations and Crisis Communication · Disaster Management and Resilience · Tropical and Extratropical Cyclones Research
