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

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 learni...

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Bibliographic Details
Published in:International journal of environmental research and public health 2023-01, Vol.20 (3), p.1862
Main Authors: Shen, Hongzhou, Ju, Yue, Zhu, Zhijing
Format: Article
Language:English
Subjects:
Algorithms
Classification
Communication
Data mining
Digital media
Disasters
Emergencies
Emergency management
Emergency preparedness
Emergency response
Emotions
Feasibility studies
Humans
Information Management
Learning algorithms
Machine Learning
Rainstorms
Social Media
Social networks
Support vector machines
User generated content
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Summary: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.
ISSN:1660-4601
1661-7827
1660-4601
DOI:10.3390/ijerph20031862