To what extent does machine learning benefit in predicting personal mental issues through social media analysis?

Abstract

Social media has become an increasingly popular platform for individuals to express their emotions and share personal experiences. This unique characteristic of social media has led to its recognition as a valuable resource for studying mental health trends at the individual level. This article investigates the application of machine learning techniques, such as sentiment analysis, emotion extraction, and topic modeling, in detecting signs of depressive symptoms within social media content. By examining and synthesizing findings from multiple studies, this review provides an analysis of the advantages and limitations of these machine learning techniques in this specific context. Furthermore, it highlights the ethical considerations that should be taken into account in future research endeavors. The integration of machine learning techniques with social media data presents a promising avenue for identifying and addressing mental health concerns, but it is essential to approach this research responsibly and ethically. The growing body of knowledge in this field can contribute to the development of effective interventions and support for individuals experiencing depressive symptoms.