Social media insights on public perception and sentiment during and after disasters: The European floods in 2021 as a case study

• Published in: Transactions in GIS 2023-09, Vol.27 (6), p.1766-1793
• Main Authors: Li, Weilian, Haunert, Jan‐Henrik, Knechtel, Julius, Zhu, Jun, Zhu, Qing, Dehbi, Youness
• Format: Article
• Language: English
• Subjects: Climate change; Data analysis; Data mining; Data processing; Decision making; Digital media; Disaster management; Disasters; Emergency preparedness; Flood predictions; Floods; Global warming; Perception; Perceptions; Predictions; Public concern; Public opinion; Risk management; Sentiment analysis; Situational awareness; Social media; Social networks; Social organization; Sustainability; Sustainable development; Sustainable Development Goals; Workflow
• ISSN: 1361-1682; 1467-9671
• DOI: 10.1111/tgis.13097

Detecting and collecting public opinion via social media can provide near real‐time information to decision‐makers, which plays a vital role in urban disaster management and sustainable development. However, there has been little work focusing on identifying the perception and the sentiment polarity expressed by users during and after disasters, particularly regional flood events. In this article, we comprehensively analyze tweets data related to the “European floods in 2021” over time, topic, and sentiment, forming a complete workflow from data processing, topic modeling, sentiment analysis, and topic and sentiment prediction. The aim is to address the following research questions: (1) What are the public perception and main concerns during and after floods? (2) How does the public sentiment change during and after floods? Results indicate that there is a significant correlation between a flood's trend and the heat of corresponding tweets. The three topics that receive the most public concern are: (1) climate change and global warming; (2) praying for the victims: and (3) disaster situations and information. Negative sentiments are predominant during the floods and will continue for some time. We tested five different classifiers, of which TextCNN‐attention turned out to deliver the best predictions in topic and sentiment prediction, and performed well for sparse flood tweets, it can be used to predict the topic and sentiment polarity of a single tweet in real‐time during the flood events. Our findings can help disaster agencies to better understand the dynamics of social networks and develop stronger situational awareness towards a disaster, which can contribute to scientifically justified decision‐making in urban risk management and also meet the challenges associated with the global sustainable development goal 11 (SDGs) on Sustainable Cities and Communities.