Publication: Facebook Social Media for Depression Detection in the Thai Community
Issued Date
2018-09-06
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2-s2.0-85057741064
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Mahidol University
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SCOPUS
Bibliographic Citation
Proceeding of 2018 15th International Joint Conference on Computer Science and Software Engineering, JCSSE 2018. (2018)
Suggested Citation
Kantinee Katchapakirin, Konlakorn Wongpatikaseree, Panida Yomaboot, Yongyos Kaewpitakkun Facebook Social Media for Depression Detection in the Thai Community. Proceeding of 2018 15th International Joint Conference on Computer Science and Software Engineering, JCSSE 2018. (2018). doi:10.1109/JCSSE.2018.8457362 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/45586
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Title
Facebook Social Media for Depression Detection in the Thai Community
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Abstract
© 2018 IEEE. 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 largescale 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.