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Characterizing Depression Issues on Sina Weibo

The prevalence of depression has increased significantly over the past few years both in developed and developing countries. However, many people with symptoms of depression still remain untreated or undiagnosed. Social media may be a tool to help researchers and clinicians to identify and support i...

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Detalles Bibliográficos
Autores principales: Tian, Xianyun, Batterham, Philip, Song, Shuang, Yao, Xiaoxu, Yu, Guang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5923806/
https://www.ncbi.nlm.nih.gov/pubmed/29659489
http://dx.doi.org/10.3390/ijerph15040764
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author Tian, Xianyun
Batterham, Philip
Song, Shuang
Yao, Xiaoxu
Yu, Guang
author_facet Tian, Xianyun
Batterham, Philip
Song, Shuang
Yao, Xiaoxu
Yu, Guang
author_sort Tian, Xianyun
collection PubMed
description The prevalence of depression has increased significantly over the past few years both in developed and developing countries. However, many people with symptoms of depression still remain untreated or undiagnosed. Social media may be a tool to help researchers and clinicians to identify and support individuals who experience depression. More than 394,000,000 postings were collected from China’s most popular social media website, Sina Weibo. 1000 randomly selected depression-related postings was coded and analyzed to learn the themes of these postings, and a text classifier was built to identify the postings indicating depression. The identified depressed users were compared with the general population on demographic characteristics, diurnal patterns, and patterns of emoticon usage. We found that disclosure of depression was the most popular theme; depression displayers were more engaged with social media compared to non-depression displayers, the depression postings showed geographical variations, depression displayers tended to be active during periods of leisure and sleep, and depression displayers used negative emoticons more frequently than non-depression displayers. This study offers a broad picture of depression references on China’s social media, which may be cost effectively developed to detect and help individuals who may suffer from depression disorders.
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spelling pubmed-59238062018-05-03 Characterizing Depression Issues on Sina Weibo Tian, Xianyun Batterham, Philip Song, Shuang Yao, Xiaoxu Yu, Guang Int J Environ Res Public Health Article The prevalence of depression has increased significantly over the past few years both in developed and developing countries. However, many people with symptoms of depression still remain untreated or undiagnosed. Social media may be a tool to help researchers and clinicians to identify and support individuals who experience depression. More than 394,000,000 postings were collected from China’s most popular social media website, Sina Weibo. 1000 randomly selected depression-related postings was coded and analyzed to learn the themes of these postings, and a text classifier was built to identify the postings indicating depression. The identified depressed users were compared with the general population on demographic characteristics, diurnal patterns, and patterns of emoticon usage. We found that disclosure of depression was the most popular theme; depression displayers were more engaged with social media compared to non-depression displayers, the depression postings showed geographical variations, depression displayers tended to be active during periods of leisure and sleep, and depression displayers used negative emoticons more frequently than non-depression displayers. This study offers a broad picture of depression references on China’s social media, which may be cost effectively developed to detect and help individuals who may suffer from depression disorders. MDPI 2018-04-16 2018-04 /pmc/articles/PMC5923806/ /pubmed/29659489 http://dx.doi.org/10.3390/ijerph15040764 Text en © 2018 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tian, Xianyun
Batterham, Philip
Song, Shuang
Yao, Xiaoxu
Yu, Guang
Characterizing Depression Issues on Sina Weibo
title Characterizing Depression Issues on Sina Weibo
title_full Characterizing Depression Issues on Sina Weibo
title_fullStr Characterizing Depression Issues on Sina Weibo
title_full_unstemmed Characterizing Depression Issues on Sina Weibo
title_short Characterizing Depression Issues on Sina Weibo
title_sort characterizing depression issues on sina weibo
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5923806/
https://www.ncbi.nlm.nih.gov/pubmed/29659489
http://dx.doi.org/10.3390/ijerph15040764
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