Extracting Useful Emergency Information from Social Media: A Method Integrating Machine Learning and Rule-Based Classification

Abstract

User-generated contents (UGCs) on social media are a valuable source of emergency information (EI) that can facilitate emergency responses. However, the tremendous amount and heterogeneous quality of social media UGCs make it difficult to extract truly useful EI, especially using pure machine learning methods. Hence, this study proposes a machine learning and rule-based integration method (MRIM) and evaluates its EI classification performance and determinants. Through comparative experiments on microblog data about the “July 20 heavy rainstorm in Zhengzhou” posted on China’s largest social media platform, we find that the MRIM performs better than pure machine learning methods and pure rule-based methods, and that its performance is influenced by microblog characteristics such as the number of words, exact address and contact information, and users’ attention. This study demonstrates the feasibility of integrating machine learning and rule-based methods to mine the text of social media UGCs and provides actionable suggestions for emergency information management practitioners.