Mental Health Analysis in Social Media Posts: A Survey

The surge in internet use to express personal thoughts and beliefs makes it increasingly feasible for the social NLP research community to find and validate associations between social media posts and mental health status. Cross-sectional and longitudinal studies of social media data bring to fore the importance of real-time responsible AI models for mental health analysis. Aiming to classify the research directions for social computing and tracking advances in the development of machine learning (ML) and deep learning (DL) based models, we propose a comprehensive survey on quantifying mental health on social media. We compose a taxonomy for mental healthcare and highlight recent attempts in examining social well-being with personal writings on social media. We define all the possible research directions for mental healthcare and investigate a thread of handling online social media data for stress, depression and suicide detection for this work. The key features of this manuscript are (i) feature extraction and classification, (ii) recent advancements in AI models, (iii) publicly available dataset, (iv) new frontiers and future research directions. We compile this information to introduce young research and academic practitioners with the field of computational intelligence for mental health analysis on social media. In this manuscript, we carry out a quantitative synthesis and a qualitative review with the corpus of over 92 potential research articles. In this context, we release the collection of existing work on suicide detection in an easily accessible and updatable repository.

Automated summary

The increasing use of the internet for expressing personal thoughts and beliefs has made it easier for the social NLP research community to identify and validate the connections between social media posts and mental health status. Cross-sectional and longitudinal studies on social media data underscore the importance of real-time responsible AI models for mental health analysis. This comprehensive survey focuses on quantifying mental health on social media, classifying research directions in social computing, tracking advances in ML and DL models, and examining social well-being through personal writings on social media. The paper outlines various research directions for mental healthcare, including the handling of online social media data for stress, depression, and suicide detection. The key features of this manuscript include feature extraction and classification, recent advancements in AI models, publicly available dataset, and future research directions. The paper aims to introduce young researchers and academic practitioners to the field of computational intelligence for mental health analysis on social media, providing a quantitative synthesis and qualitative review of over 92 potential research articles. In addition, the paper releases a collection of existing work on suicide detection in an easily accessible and updatable repository.