# Extracting public opinion on typhoon disasters in China: a sina weibo case study of landfalling typhoon Muifa (2022)

**Authors:** Yanran Sun, Qian Wang, Yongchang Zhu, Jing Xu, Lu Liu, Chunyi Xiang, Chuanhai Qian

PMC · DOI: 10.1038/s41598-026-40736-8 · 2026-02-23

## 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.

## Key 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 Liaoning (q < 0.05). Negative sentiment was highly correlated with rising precipitation, a trend largely driven by discussions within the typhoon impact topic category. Our findings underscore the potential of social media as a real-time indicator of localized public sentiment during disasters, with official risk narratives playing a key role in shaping attention. This study offers insights that may inform targeted risk communication and emergency management strategies.

## Full-text entities

- **Diseases:** anxiety (MESH:D001007), COVID-19 pandemic (MESH:D000086382), PADM (MESH:D009207), panic (MESH:D016584), TS (MESH:C566109)
- **Chemicals:** water (MESH:D014867), TC (-)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Figures

12 figures with captions in the complete paper: https://tomesphere.com/paper/PMC13031805/full.md

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Source: https://tomesphere.com/paper/PMC13031805