Publication: Social behavior analysis and Thai mental health questionnaire (TMHQ) optimization for depression detection system
Issued Date
2020-01-01
Resource Type
ISSN
17451361
09168532
09168532
Other identifier(s)
2-s2.0-85082753710
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Mahidol University
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SCOPUS
Bibliographic Citation
IEICE Transactions on Information and Systems. Vol.E103D, No.4 (2020), 771-778
Suggested Citation
Konlakorn Wongaptikaseree, Panida Yomaboot, Kantinee Katchapakirin, Yongyos Kaewpitakkun Social behavior analysis and Thai mental health questionnaire (TMHQ) optimization for depression detection system. IEICE Transactions on Information and Systems. Vol.E103D, No.4 (2020), 771-778. doi:10.1587/transinf.2019IIP0003 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/54530
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Title
Social behavior analysis and Thai mental health questionnaire (TMHQ) optimization for depression detection system
Abstract
© 2020 The Institute of Electronics, Information and Communication Engineers. Depression is a major mental health problem in Thailand. The depression rates have been rapidly increasing. Over 1.17 million Thai people suffer from this mental illness. It is important that a reliable depression screening tool is made available so that depression could be early detected. 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 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. To establish the reliable result, Thai Mental Health Questionnaire (TMHQ), a standardized psychological inventory that measures major mental health problems including depression. Depression scale of the TMHQ comprises of 20 items, is used as the baseline for concluding the result. Furthermore, this study also aims to do factor analysis and reduce the number of depression items. Data was collected from over 600 Facebook users. Descriptive statistics, Exploratory Factor Analysis, and Internal consistency were conducted. Results provide the optimized version of the TMHQ-depression that contain 9 items. The 9 items are categorized into four factors which are suicidal ideation, sleep problems, anhedonic, and guilty feelings. Internal consistency analysis shows that this short version of the TMHQ-depression has good to excellent reliability (Cronbach's alpha > .80). The findings suggest that this optimized TMHQ-depression questionnaire holds a good psychometric property and can be used for depression detection.