Detection of Suicidal Tendency in Users by Analysing the Twitter Posts

Abstract

Suicidal conceptual detection in online social networks is a prominent research area with most challenges. The main challenge of suicide prevention is understanding and detecting the major threat level and warning signs that may trigger the event. In this paper, we present a new way that uses media platforms like Twitter to measure suicide signs for a single person and to identify posts or comments containing suicidal intentions. The main aim is to identify such intentions in user posts to detect that we use a general language-based technique like martingale framework with calculating user online behavior. Practically proved that our text-scoring approach identifies warning signs in the text compared to traditional machine learning. Moreover, the applications of the martingale framework focus on any change in online behavior and eventually detect behavioral changes.