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 generating an emergency material demand index from social media that takes into account both sentiment intensity and social influence. Negative sentiment and social influence are integrated to generate review-level demand intensity and further aggregated into a dynamic MDI. Using mask demand during the early COVID-19 outbreak as a case study, 3,323,151 Weibo reviews were collected, with a 20% temporally stratified sample used for MDI generation. The domain-adapted RoBERTa achieved a negative F1-score of 0.79 and a Macro-F1 of 0.83, while sensitivity analysis confirmed the robustness of the MDI. External comparison suggests a potential lagged association with subsequent material distribution, and rolling-origin forecasting demonstrates its applicability to short-term demand forecasting. Our study provides a tool for dynamically monitoring and forecasting emergency materials demand to ensure sufficient time for the production or distribution of emergency materials.