Facebook Social Media for Depression Detection in the Thai Community

被引:0
|
作者
Katchapakirin, Kantinee [1 ]
Wongpatikaseree, Konlakorn [2 ]
Yomaboot, Panida [3 ]
Kaewpitakkun, Yongyos [4 ]
机构
[1] TOT Publ Co Ltd, Bangkok, Thailand
[2] Mahidol Univ, Dept Comp Engn, Bangkok, Thailand
[3] Mahidol Univ, Dept Psychiat, Bangkok, Thailand
[4] Telenor Grp, Bangkok, Thailand
关键词
depression detection; depression screening; psychological tool; social media mental health; health tech;
D O I
暂无
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Depression is one of the leading mental health problems. It is a cause of psychological disability and economic burden to a country. Around 1.5 Thai people suffer from depression and its prevalence has been growing up fast. Although it is a serious psychological problem, less than a half of those who have this emotional problem gained access to mental health service. This could be a result of many factors including having lack awareness about the disease. One of the solutions would be providing a tool that depression could be easily and early detected. This would help people to be aware of their emotional states and seek help from professional services. Given Facebook is the most popular social network platform in Thailand, it could be a large-scale resource to develop a depression detection tool. This research employs Natural Language Processing (NLP) techniques to develop a depression detection algorithm for the Thai language on Facebook where people use it as a tool for sharing opinions, feelings, and life events. Results from 35 Facebook users indicated that Facebook behaviours could predict depression level.
引用
收藏
页码:227 / 231
页数:5
相关论文
共 50 条
  • [1] Detection of Depression in Thai Social Media Messages using Deep Learning
    Kumnunt, Boriharn
    Sornil, Ohm
    PROCEEDINGS OF THE 1ST INTERNATIONAL CONFERENCE ON DEEP LEARNING THEORY AND APPLICATIONS (DELTA), 2020, : 111 - 118
  • [3] Facebook and virtual nationhood: social media and the Arab Canadians community
    Ahmed Al-Rawi
    AI & SOCIETY, 2019, 34 : 559 - 571
  • [4] Community Detection and Mining in Social Media
    Tang, Lei
    Liu, Huan
    Synthesis Lectures on Data Mining and Knowledge Discovery, 2010, 2 (01): : 1 - 137
  • [5] Understanding Depression Detection Using Social Media
    Latif, Aliza Abdul
    Cob, Zaihisma Che
    Drus, Sulfeeza Mohd
    Anwar, Rina Md
    Radzi, Husni Mohd
    6TH IEEE INTERNATIONAL CONFERENCE ON RECENT ADVANCES AND INNOVATIONS IN ENGINEERING (ICRAIE), 2021,
  • [6] Feature Engineering for Depression Detection in Social Media
    Stankevich, Maxim
    Isakov, Vadim
    Devyatkin, Dmitry
    Smirnov, Ivan
    PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION APPLICATIONS AND METHODS (ICPRAM 2018), 2018, : 426 - 431
  • [7] Explainable depression symptom detection in social media
    Bao, Eliseo
    Perez, Anxo
    Parapar, Javier
    HEALTH INFORMATION SCIENCE AND SYSTEMS, 2024, 12 (01):
  • [8] Depression Detection on Social Media with Reinforcement Learning
    Gui, Tao
    Zhang, Qi
    Zhu, Liang
    Zhou, Xu
    Peng, Minlong
    Huang, Xuanjing
    CHINESE COMPUTATIONAL LINGUISTICS, CCL 2019, 2019, 11856 : 613 - 624
  • [9] Measuring the Latency of Depression Detection in Social Media
    Sadeque, Farig
    Xu, Dongfang
    Bethard, Steven
    WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, 2018, : 495 - 503
  • [10] Fair and Explainable Depression Detection in Social Media
    Adarsh, V
    Kumar, P. Arun
    Lavanya, V
    Gangadharan, G. R.
    INFORMATION PROCESSING & MANAGEMENT, 2023, 60 (01)