STP:Suicidal tendency prediction among the youth using social network data

Abstract

The suicide tendency is increasing day to day. It is having a negative impact on our youth. Human at possibility of suicide does not want help before trying to attempt and do not feel necessity to any mental health counselling. As a result, suicidal tendency is becoming social challenge. At the same time, social media are becoming popular for communication and exchange emotional expression to the world. Social media like Twitter, Facebook and Instagram play major role for emotion sharing. Therefore, huge number of people publishes their emotion of depression and happiness within notes in these social media like Twitter. Social platform like Twitter has a large number of collection of emotional notes. In this situation, machine learning helps in early prediction of the depression and suicidal tendency. Therefore, in this paper, we develop a soft solution which is able to early detect suicidal tendency among the youth. In this approach, we train a model with genuine suicidal notes and post collected from different sources and generate score for input social media tweet high or low for social tendency prediction. With the help of social media postings like Twitter, we are able to identify risk of suicide.