Suicidal Ideation Detection from social media: A Detailed Review of Machine Learning and Deep Learning

Abstract

Social networks are crucial tools for learning about people's attitudes towards various issues since they allow them to express their thoughts to friends and family. Recent years have seen considerable challenges in natural language processing (NLP) and psychology regarding the detection of suicidal ideation via online social network analysis. The complex early signs of suicide ideations can be identified with the proper use of social media information, which can afterwards save many lives. Even though numerous strategies have been used over the past few decades to identify suicidal thoughts, machine learning (ML) and deep learning (DL) methods offer more insightful results. So, in this study, we examine several cutting-edge ML and DL approaches for detecting suicidal ideation. We also observe a few significant issues in the entire corpus of literature that could be explored in further research. Finally, we hope this study will shed light on critical issues in identifying suicidal thoughts on social media for readers and ML and DL researchers.