Characterizing Depression Issues on Sina Weibo

被引:28
|
作者
Tian, Xianyun [1 ]
Batterham, Philip [2 ]
Song, Shuang [1 ]
Yao, Xiaoxu [1 ]
Yu, Guang [1 ]
机构
[1] Harbin Inst Technol, Sch Management, Harbin 150001, Heilongjiang, Peoples R China
[2] Australian Natl Univ, Ctr Mental Hlth Res, Canberra, ACT 2601, Australia
基金
中国国家自然科学基金;
关键词
social media; mental health; public health; depression; Sina Weibo; COLLEGE-STUDENTS; SOCIAL MEDIA; DISORDERS; EPIDEMIOLOGY; DISCLOSURES; SYMPTOMS; HIV/AIDS; FACEBOOK; POSTINGS; PEOPLE;
D O I
10.3390/ijerph15040764
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:11
相关论文
共 50 条
  • [1] Characterizing Eating Disorder Issues on Sina Weibo
    Tang, Jingyun
    Yu, Guang
    Wang, Zheng
    Yao, Xiaoxu
    [J]. FUZZY SYSTEMS AND DATA MINING VI, 2020, 331 : 722 - 731
  • [2] Characterizing Tweeting Behaviors of Sina Weibo Users via Public Data Streaming
    Zhang, Kai
    Yu, Qian
    Lei, Kai
    Xu, Kuai
    [J]. WEB-AGE INFORMATION MANAGEMENT, WAIM 2014, 2014, 8485 : 294 - 297
  • [3] Patterns and Longitudinal Changes in Negative Emotions of People with Depression on Sina Weibo
    Yao, Xiaoxu
    Yu, Guang
    Tian, Xianyun
    Tang, Jingyun
    [J]. TELEMEDICINE AND E-HEALTH, 2020, 26 (06) : 734 - 743
  • [4] Detecting Spam on Sina Weibo
    Ma, Yingcai
    Niu, Yan
    Ren, Yan
    Xue, Yibo
    [J]. PROCEEDINGS OF THE 1ST INTERNATIONAL WORKSHOP ON CLOUD COMPUTING AND INFORMATION SECURITY (CCIS 2013), 2013, 52 : 404 - 407
  • [5] A Study on Strength of Sina Weibo
    Chen Fu
    Zhan Shaobin
    Shi Guangjun
    [J]. INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING, 2014, 7 (03): : 199 - 204
  • [6] Natural Language Processing for Depression Prediction on Sina Weibo: Method Study and Analysis
    Zhang, Zhenwen
    Zhu, Jianghong
    Guo, Zhihua
    Zhang, Yu
    Li, Zepeng
    Hu, Bin
    [J]. JMIR MENTAL HEALTH, 2024, 11
  • [7] An analysis of sleep complaints on Sina Weibo
    Tian, Xianyun
    Yu, Guang
    He, Fang
    [J]. COMPUTERS IN HUMAN BEHAVIOR, 2016, 62 : 230 - 235
  • [8] Automatic Rumors Identification on Sina Weibo
    Liang, Gang
    Yang, Jin
    Xu, Chun
    [J]. 2016 12TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD), 2016, : 1523 - 1531
  • [9] Opinioned Post Detection in Sina Weibo
    Lv, Yanzhang
    Liu, Jun
    Chen, Hao
    Mi, Jianhong
    Liu, Mengyue
    Zheng, Qinghua
    [J]. IEEE ACCESS, 2017, 5 : 7263 - 7271
  • [10] Detection of Zombie Followers in SINA Weibo
    Zhang, Zhedi
    Zou, Futai
    Pan, Li
    Pei, Bei
    Li, Jianhua
    [J]. 2016 2ND IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATIONS (ICCC), 2016, : 2476 - 2480