Systematic Review of Learning Models for Suicidal Ideation on Social Media

Abstract

In present-day society, the major critical issues are mental health problems which eventually turn out to be suicidal ideation. Premature detection of suicidal thoughts among people is the solution to avoid suicide in the latter times. With each passing year, the growth rate of suicides is abruptly increasing. The social media platform is the key from which people around the world come across and share their feelings, emotions, and reaction to what they are going through in their lives. The data which is available on social media can be used for the identification and detection of suicidal ideation among people, on social media forums like Twitter, Reddit, Tumblr, Facebook, and Instagram. Natural Language Processing has come a long way in the research of finding the sentiment of individuals and checking the linguistic patterns of text shared by the people. Effective to carry out the research by using machine learning, deep learning, and transfer learning algorithms on the online forum data of people. Algorithms such as SVM, Logistic Regression, Naive Bayes, Convolution Neural Network, Recurrent Neural Network, Bidirectional Long Short-Term Memory, BERT, and RoBERTa, respectively, were enforced to classify or detect the suicides in the social media data.