Cargando…

Health-Related Hot Topic Detection in Online Communities Using Text Clustering

Recently, health-related social media services, especially online health communities, have rapidly emerged. Patients with various health conditions participate in online health communities to share their experiences and exchange healthcare knowledge. Exploring hot topics in online health communities...

Descripción completa

Detalles Bibliográficos
Autores principales: Lu, Yingjie, Zhang, Pengzhu, Liu, Jingfang, Li, Jia, Deng, Shasha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3574139/
https://www.ncbi.nlm.nih.gov/pubmed/23457530
http://dx.doi.org/10.1371/journal.pone.0056221
_version_ 1782259573132886016
author Lu, Yingjie
Zhang, Pengzhu
Liu, Jingfang
Li, Jia
Deng, Shasha
author_facet Lu, Yingjie
Zhang, Pengzhu
Liu, Jingfang
Li, Jia
Deng, Shasha
author_sort Lu, Yingjie
collection PubMed
description Recently, health-related social media services, especially online health communities, have rapidly emerged. Patients with various health conditions participate in online health communities to share their experiences and exchange healthcare knowledge. Exploring hot topics in online health communities helps us better understand patients’ needs and interest in health-related knowledge. However, the statistical topic analysis employed in previous studies is becoming impractical for processing the rapidly increasing amount of online data. Automatic topic detection based on document clustering is an alternative approach for extracting health-related hot topics in online communities. In addition to the keyword-based features used in traditional text clustering, we integrate medical domain-specific features to represent the messages posted in online health communities. Three disease discussion boards, including boards devoted to lung cancer, breast cancer and diabetes, from an online health community are used to test the effectiveness of topic detection. Experiment results demonstrate that health-related hot topics primarily include symptoms, examinations, drugs, procedures and complications. Further analysis reveals that there also exist some significant differences among the hot topics discussed on different types of disease discussion boards.
format Online
Article
Text
id pubmed-3574139
institution National Center for Biotechnology Information
language English
publishDate 2013
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-35741392013-03-01 Health-Related Hot Topic Detection in Online Communities Using Text Clustering Lu, Yingjie Zhang, Pengzhu Liu, Jingfang Li, Jia Deng, Shasha PLoS One Research Article Recently, health-related social media services, especially online health communities, have rapidly emerged. Patients with various health conditions participate in online health communities to share their experiences and exchange healthcare knowledge. Exploring hot topics in online health communities helps us better understand patients’ needs and interest in health-related knowledge. However, the statistical topic analysis employed in previous studies is becoming impractical for processing the rapidly increasing amount of online data. Automatic topic detection based on document clustering is an alternative approach for extracting health-related hot topics in online communities. In addition to the keyword-based features used in traditional text clustering, we integrate medical domain-specific features to represent the messages posted in online health communities. Three disease discussion boards, including boards devoted to lung cancer, breast cancer and diabetes, from an online health community are used to test the effectiveness of topic detection. Experiment results demonstrate that health-related hot topics primarily include symptoms, examinations, drugs, procedures and complications. Further analysis reveals that there also exist some significant differences among the hot topics discussed on different types of disease discussion boards. Public Library of Science 2013-02-15 /pmc/articles/PMC3574139/ /pubmed/23457530 http://dx.doi.org/10.1371/journal.pone.0056221 Text en © 2013 Lu et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Lu, Yingjie
Zhang, Pengzhu
Liu, Jingfang
Li, Jia
Deng, Shasha
Health-Related Hot Topic Detection in Online Communities Using Text Clustering
title Health-Related Hot Topic Detection in Online Communities Using Text Clustering
title_full Health-Related Hot Topic Detection in Online Communities Using Text Clustering
title_fullStr Health-Related Hot Topic Detection in Online Communities Using Text Clustering
title_full_unstemmed Health-Related Hot Topic Detection in Online Communities Using Text Clustering
title_short Health-Related Hot Topic Detection in Online Communities Using Text Clustering
title_sort health-related hot topic detection in online communities using text clustering
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3574139/
https://www.ncbi.nlm.nih.gov/pubmed/23457530
http://dx.doi.org/10.1371/journal.pone.0056221
work_keys_str_mv AT luyingjie healthrelatedhottopicdetectioninonlinecommunitiesusingtextclustering
AT zhangpengzhu healthrelatedhottopicdetectioninonlinecommunitiesusingtextclustering
AT liujingfang healthrelatedhottopicdetectioninonlinecommunitiesusingtextclustering
AT lijia healthrelatedhottopicdetectioninonlinecommunitiesusingtextclustering
AT dengshasha healthrelatedhottopicdetectioninonlinecommunitiesusingtextclustering