Facebook Social Media for Depression Detection in the Thai community

EasyChair Preprint 246

Abstract

With a lifetime prevalence of 2.4%, 1.5 million Thai people suffer from depression in their lifetime. Depression is the leading cause of disability and a major contributor to the overall burden of disease. With the lack of response to this health, the challenge has been compounded by negative perception of mental illness, less than half of depression individuals in Thailand access mental health services. This results in increased number of depression by about 18%. More proactive service should be considered. The sooner depression is detected, the less complicated and shorter course of therapy it would be. This research applies Natural Language Processing (NLP) techniques in psychological domains to develop a depression detection algorithm for the Thai language. Since Facebook is the most popular social network in Thailand. It is often used for sharing opinions and life events. This research proposes Facebook utilization as a large-scale resource to develop depression screening model. The features for depression detection in the Thai community are recognized.

Keyphrases: Depression Detection, Psychological Tool, depression screening, health tech, social media mental health

@booklet{EasyChair:246,
  author    = {Kantinee Katchapakirin and Konlakorn Wongpatikaseree and Panida Yomaboot and Yongyos Kaewpitakkun},
  title     = {Facebook Social Media  for Depression Detection in the Thai community},
  doi       = {10.29007/tscc},
  howpublished = {EasyChair Preprint 246},
  year      = {EasyChair, 2018}}