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The crowdsourcing mode of public participation in emergency management is a new method that appears in recent years, and a comprehensive discussion and analysis of this new mode is helpful to its further improvement. This paper firstly presents a detailed analysis of the concept, components and processes of crowdsourcing, and discusses the background and origin reason of the combination of public participation in emergency management and crowdsourcing. Then, this study, using the case study method, deeply analyzes the successful application of a crowdsourcing platform named Ushahidi in the 2010 earthquake in Haiti from the aspects of source, processing and utilization of emergency information. Finally, this paper sums up some frontier topics that need a further research in the crowdsourcing mode of public participation in emergency management.

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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.

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