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

Business

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

H. Shen, Y. Ju, et al.

This study introduces an innovative machine learning and rule-based integration method (MRIM) for extracting valuable emergency information from social media content. Conducted by Hongzhou Shen, Yue Ju, and Zhijing Zhu, the research showcases the effectiveness of MRIM over traditional methods during the Zhengzhou rainstorm, providing insightful implications for emergency management.

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Playback language: English
Abstract
This study proposes a machine learning and rule-based integration method (MRIM) to extract useful emergency information (EI) from social media user-generated content (UGC). The MRIM outperforms pure machine learning and rule-based methods in classifying EI from microblog data about the July 2021 Zhengzhou rainstorm. The study's findings highlight the impact of microblog characteristics, such as word count, address and contact information, and user attention, on the performance of the MRIM. The research demonstrates the feasibility of integrating machine learning and rule-based methods for effective EI extraction and provides actionable suggestions for emergency information management.
Publisher
International Journal of Environmental Research and Public Health
Published On
Jan 19, 2023
Authors
Hongzhou Shen, Yue Ju, Zhijing Zhu
Tags
machine learning
rule-based integration
emergency information
social media
Zhengzhou rainstorm
user-generated content
information management
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