Characterizing Depression Issues on Sina Weibo

Xianyun Tian, Philip Batterham, Shuang Song, Xiaoxu Yao and Guang Yu
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Xianyun Tian: School of Management, Harbin Institute of Technology, Harbin 150001, China
Philip Batterham: Centre for Mental Health Research, The Australian National University, Canberra ACT 2601, Australia
Shuang Song: School of Management, Harbin Institute of Technology, Harbin 150001, China
Xiaoxu Yao: School of Management, Harbin Institute of Technology, Harbin 150001, China
Guang Yu: School of Management, Harbin Institute of Technology, Harbin 150001, China

IJERPH, 2018, vol. 15, issue 4, 1-11

Abstract: 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.

Keywords: social media; mental health; public health; depression; Sina Weibo (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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