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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9915315/ https://www.ncbi.nlm.nih.gov/pubmed/36767235 http://dx.doi.org/10.3390/ijerph20031862 |
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author | Shen, Hongzhou Ju, Yue Zhu, Zhijing |
author_facet | Shen, Hongzhou Ju, Yue Zhu, Zhijing |
author_sort | Shen, Hongzhou |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9915315 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99153152023-02-11 Extracting Useful Emergency Information from Social Media: A Method Integrating Machine Learning and Rule-Based Classification Shen, Hongzhou Ju, Yue Zhu, Zhijing Int J Environ Res Public Health Article 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. MDPI 2023-01-19 /pmc/articles/PMC9915315/ /pubmed/36767235 http://dx.doi.org/10.3390/ijerph20031862 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Shen, Hongzhou Ju, Yue Zhu, Zhijing Extracting Useful Emergency Information from Social Media: A Method Integrating Machine Learning and Rule-Based Classification |
title | Extracting Useful Emergency Information from Social Media: A Method Integrating Machine Learning and Rule-Based Classification |
title_full | Extracting Useful Emergency Information from Social Media: A Method Integrating Machine Learning and Rule-Based Classification |
title_fullStr | Extracting Useful Emergency Information from Social Media: A Method Integrating Machine Learning and Rule-Based Classification |
title_full_unstemmed | Extracting Useful Emergency Information from Social Media: A Method Integrating Machine Learning and Rule-Based Classification |
title_short | Extracting Useful Emergency Information from Social Media: A Method Integrating Machine Learning and Rule-Based Classification |
title_sort | extracting useful emergency information from social media: a method integrating machine learning and rule-based classification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9915315/ https://www.ncbi.nlm.nih.gov/pubmed/36767235 http://dx.doi.org/10.3390/ijerph20031862 |
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