A Social Media-Driven Public Participatory Emergency Decision-Making Method Considering Sentiment and Social Influence

In the data intelligence era, social media platforms have supplemented emergency decision-making with a wealth of timely data, offering new research paradigms for emergency response. It is crucial to identify and predict the emergency material demand for reducing secondary damage during emergencies. This study aims to fill the research gap in analysing the emergency material demand using real-time social media data. We propose a method for mining an emergency material demand index from social media that takes into account both sentiment intensity and social influence. Furthermore, a real-time material demand forecasting method combined the material demand indexes and Holter-Winter procedure is designed. Using this method, we analysed 3,323,151 tweets on the Weibo Public Opinion Datasets from Dec. 1, 2019, to Apr. 30, 2020, to monitor and forecast the masks demand during the early stages of COVID-19 outbreak. For our mining method, we compare its result with the Wuhan Red Cross Society’s mask distribution data during March 2020, and the index can capture changes in mask demand during certain periods. Our forecasting method outperforms the two baseline models, with a RMSE of 2.58% and a MAPE of 3.01%. Our study provides a tool for dynamically monitoring and forecasting emergency materials demand to ensures sufficient time for the production or distribution of emergency materials.