School of Management, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
Research Center for Information Industry Integration, Innovation and Emergency Management, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
Int J Environ Res Public Health. 2023 Jan 19;20(3):1862. doi: 10.3390/ijerph20031862.
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.
社交媒体上的用户生成内容（UGC）是一种有价值的紧急信息（EI）来源，可以促进应急响应。然而，社交媒体 UGC 的数量巨大且质量参差不齐，这使得很难提取真正有用的 EI，尤其是使用纯机器学习方法。因此，本研究提出了一种机器学习和基于规则的集成方法（MRIM），并评估了其 EI 分类性能和决定因素。通过在中国最大的社交媒体平台上发布的关于“郑州 2021 年 7 月暴雨”的微博数据的对比实验，我们发现 MRIM 比纯机器学习方法和纯基于规则的方法表现更好，其性能受到微博特征的影响，如字数、确切地址和联系方式以及用户关注度。本研究证明了将机器学习和基于规则的方法集成起来挖掘社交媒体 UGC 文本的可行性，并为应急信息管理从业者提供了可行的建议。
