Decoding depression: Analyzing social network insights for depression severity assessment with transformers and explainable AI

Abstract

Depression is a mental state characterized by recurrent feelings of melancholy, hopelessness, and disinterest in activities, having a significant negative influence on everyday functioning and general well-being. Millions of users express their thoughts and emotions on social media platforms, which can be used as a rich source of data for early detection of depression. In this connection, this work leverages an ensemble of transformer-based architectures for quantifying the severity of depression from social media posts into four categories- non-depressed, mild, moderate, and severe. At first, a diverse range of preprocessing techniques is employed to enhance the quality and relevance of the input. Then, the preprocessed samples are passed through three variants of transformer-based models, namely vanilla BERT, BERTweet, and ALBERT, for generating predictions, which are combined using a weighted soft-voting approach. We conduct a comprehensive explainability analysis to gain deeper insights into the decision-making process, examining both local and global perspectives. Furthermore, to the best of our knowledge, we are the first ones to explore the extent to which a Large Language Model (LLM) like ‘ChatGPT’ can perform this task. Evaluation of the model on the publicly available ‘DEPTWEET’ dataset produces state-of-the-art performance with 13.5% improvement in AUC-ROC score.