Suicidal ideation is one of the severe mental health issues and a serious social problem faced by our society. This problem has been usually dealt with through the psychological point of view, using clinical face to face settings. There are various risk factors associated with suicides, including social isolation, anxiety, depression, etc., that decrease the threshold for suicide. The COVID-19 pandemic further increases social isolation, posing a great threat to the human population. Posting suicidal thoughts on social media is gaining much attention due to the social stigma associated with the mental health. Online Social Networks (OSN) are increasingly used to express the suicidal thoughts. Recently, a top Indian actor industry took the harsh step of suicide. The last Instagram posts revealed signs of depression, which if anticipated could have saved the precious life. Recent research indicated that the public information on social media provides valuable insights on detecting the users with the suicidal ideation. The motive of this study is to provide a systematic review of the work done already in the use of social media for suicide prevention and propose a novel classification approach that classifies the suicide related tweets/ posts into three levels of distress. Moreover, our proposed classification task which was implemented through various machine learning techniques revealed high accuracy in classifying the suicidal posts. Among all algorithms, the best performing algorithm was that of the decision tree, with an F1 score ranging 0.95-0.97. After thoroughly studying the work achieved by different researchers in the area of suicide prevention, our study critically analyses those works and finds various research gaps and solves some of them. We believe that our work will motivate research community to look into other gaps that will in turn help psychiatrists, psychologists, and counsellors to protect individuals suffering from suicidal ideation.
Quantifying Suicidal Ideation on Social Media using Machine Learning: A Critical Review