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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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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. |
format | Online Article Text |
id | pubmed-5923806 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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