Suicide & Depression Detection Using Machine Learning

Abstract

Nowadays, depression is the most common mental-health issue and the leading cause of suicide and self-injurious behavior. Due to social stigma and lack of knowledge, clinical diagnosis of various mental health conditions is costly and often disregarded. These days, people choose to communicate using online social media platforms to convey their ideas, sentiments, and emotions. As a result, non-clinical mental health screening and surveillance can be facilitated by using user-generated information from online social media platforms. For quite some time, social media data has been utilized to automatically identify mental health problems through the use of traditional machine learning and natural language processing approaches. And the goal of our research is to examine how Machine Learning methods may be applied to the early detection and non-clinical, predictive diagnosis. To the best of our knowledge, we did not find any systematic literature review that studies the applications of machine learning techniques in this domain. In order to address this research gap, we conducted a systematic literature review relevant research studies published until date that have applied machine learning techniques for detecting depression and suicide or self-harm behavior from social media content. Our work comprehensively covers state-of-the-art WRT. techniques, features, datasets, and performance metrics, which can be of great value to researchers. We enumerate all the available datasets in this domain and discuss their characteristics. We also discuss the research gaps, challenges, and future research directions for advancing & catalyzing research in this domain. To the best of our knowledge, our study is the only and the most recent survey for this domain covering the latest research published until date. Based on our learnings from this review, we have also proposed a framework for mental health surveillance. We believe the findings of our work will be beneficial for researchers working in this domain.