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COVID-19 personal health mention detection from tweets using dual convolutional neural network
Twitter offers extensive and valuable information on the spread of COVID-19 and the current state of public health. Mining tweets could be an important supplement for public health departments in monitoring the status of COVID-19 in a timely manner and taking the appropriate actions to minimize its...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8976569/ https://www.ncbi.nlm.nih.gov/pubmed/35399189 http://dx.doi.org/10.1016/j.eswa.2022.117139 |
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author | Luo, Linkai Wang, Yue Liu, Hai |
author_facet | Luo, Linkai Wang, Yue Liu, Hai |
author_sort | Luo, Linkai |
collection | PubMed |
description | Twitter offers extensive and valuable information on the spread of COVID-19 and the current state of public health. Mining tweets could be an important supplement for public health departments in monitoring the status of COVID-19 in a timely manner and taking the appropriate actions to minimize its impact. Identifying personal health mentions (PHM) is the first step of social media public health surveillance. It aims to identify whether a person’s health condition is mentioned in a tweet, and it serves as a crucial method in tracking pandemic conditions in real time. However, social media texts contain noise, many creative and novel phrases, sarcastic emoji expressions, and misspellings. In addition, the class imbalance issue is usually very serious. To address these challenges, we built a COVID-19 PHM dataset containing more than 11,000 annotated tweets, and we proposed a dual convolutional neural network (CNN) framework using this dataset. An auxiliary CNN in the dual CNN structure provides supplemental information for the primary CNN in order to detect PHMs from tweets more effectively. The experiment shows that the proposed structure could alleviate the effect of class imbalance and could achieve promising results. This automated approach could monitor public health in real time and save disease-prevention departments from the tedious manual work in public health surveillance. |
format | Online Article Text |
id | pubmed-8976569 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89765692022-04-04 COVID-19 personal health mention detection from tweets using dual convolutional neural network Luo, Linkai Wang, Yue Liu, Hai Expert Syst Appl Article Twitter offers extensive and valuable information on the spread of COVID-19 and the current state of public health. Mining tweets could be an important supplement for public health departments in monitoring the status of COVID-19 in a timely manner and taking the appropriate actions to minimize its impact. Identifying personal health mentions (PHM) is the first step of social media public health surveillance. It aims to identify whether a person’s health condition is mentioned in a tweet, and it serves as a crucial method in tracking pandemic conditions in real time. However, social media texts contain noise, many creative and novel phrases, sarcastic emoji expressions, and misspellings. In addition, the class imbalance issue is usually very serious. To address these challenges, we built a COVID-19 PHM dataset containing more than 11,000 annotated tweets, and we proposed a dual convolutional neural network (CNN) framework using this dataset. An auxiliary CNN in the dual CNN structure provides supplemental information for the primary CNN in order to detect PHMs from tweets more effectively. The experiment shows that the proposed structure could alleviate the effect of class imbalance and could achieve promising results. This automated approach could monitor public health in real time and save disease-prevention departments from the tedious manual work in public health surveillance. Elsevier Ltd. 2022-08-15 2022-04-02 /pmc/articles/PMC8976569/ /pubmed/35399189 http://dx.doi.org/10.1016/j.eswa.2022.117139 Text en © 2022 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Luo, Linkai Wang, Yue Liu, Hai COVID-19 personal health mention detection from tweets using dual convolutional neural network |
title | COVID-19 personal health mention detection from tweets using dual convolutional neural network |
title_full | COVID-19 personal health mention detection from tweets using dual convolutional neural network |
title_fullStr | COVID-19 personal health mention detection from tweets using dual convolutional neural network |
title_full_unstemmed | COVID-19 personal health mention detection from tweets using dual convolutional neural network |
title_short | COVID-19 personal health mention detection from tweets using dual convolutional neural network |
title_sort | covid-19 personal health mention detection from tweets using dual convolutional neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8976569/ https://www.ncbi.nlm.nih.gov/pubmed/35399189 http://dx.doi.org/10.1016/j.eswa.2022.117139 |
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