Abstract
Globally, hundreds of millions of people are estimated living with depression reported by the World Health Organization (WHO). Even though medical technology has improved, a large proportion of sufferers are still receiving improper diagnosis and treatment. Mental illness is endured a burden interfering with emotions, feelings, and various aspects of life. It is a complex disorder that considerably affects physical health particularly severe headache, eating disorder, weakened the immune system, and sleeping disruptions on a constant basis. The traditional procedures for mental illness diagnosis typically rely solely on depression test, self-reported, and family-reported on unusual behaviors. Mental illness is considered taboo to be discussed openly hence the reluctance to seek medical attention. Thus, social media is an ideal alternative for mental illness detection by identifying the symptoms from the users’ activities on social media. In this chapter, the related studies of mental illness on social media are explored and discussed. From previous work, online users with mental health problem have been spotted taking depression screening tests, participation in online forums, and often sharing about themselves on social media. The patterns of linguistic style extracted from selected techniques are used to distinguish mentally ill users from the virtual population. The trained models will assist to classify depressed users and prospective depression users through automated monitoring system on social media.
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Ismail, N.H., Du, M., Hu, X. (2019). Social Media and Psychological Disorder. In: Bian, J., Guo, Y., He, Z., Hu, X. (eds) Social Web and Health Research. Springer, Cham. https://doi.org/10.1007/978-3-030-14714-3_9
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