Suicide Ideation Detection: A Comparative Study of Sequential and Transformer Hybrid Algorithms

Suicide is turning out to be one of the most dangerous health hazards in today’s fast paced world and is one of the leading causes of deaths among general population. Unfortunately, it also happens to be one of the most ignored factors when we compare it against other causes of fatality like road accidents, terminal illness, crimes etc. It is well and truly turning out to become a silent pandemic. Suicide ideation is commonly referred to someone having suicidal tendencies which may include, thoughts, planning, enactment, failed attempts etc. Social media platforms such as Reddit allow a relatively safe and secure space to express any sufferings without the anxiety of peer-to-peer communication or judgement and in many cases, anonymity. The study attempts to apply deep feature extraction based learning techniques on cherry-picked Kaggle dataset from r/SuicideWatch which includes reddit posts by users that contain suicide ideation which is combined with reddit posts from other domains. These modelling techniques look out for sentimental phrases, vocabulary patterns in suicidal posts, grammatical similarities and preferences of such posts like use of parts of speech and references to various entities. The end goal is to propose a model which can build upon the knowledge of existing social media content and facilitate early detection of suicide ideation in similar content in future. The study involves a comparative analysis of the most sequential and transformer-based algorithms to achieve near optimal results. The primary focus is on developing models which can correctly classify suicide ideation texts thereby minimizing false negatives to prevent loss of lives as a result of suicide.