Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Shen, Hongzhou, Ju, Yue, Zhu, Zhijing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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
_version_ 1784885874937298944
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
work_keys_str_mv AT shenhongzhou extractingusefulemergencyinformationfromsocialmediaamethodintegratingmachinelearningandrulebasedclassification
AT juyue extractingusefulemergencyinformationfromsocialmediaamethodintegratingmachinelearningandrulebasedclassification
AT zhuzhijing extractingusefulemergencyinformationfromsocialmediaamethodintegratingmachinelearningandrulebasedclassification