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In recent years, there has been a surge of interest in research on automaticmental health detection (MHD) from social media data leveraging advances innatural language processing and machine learning techniques. While significantprogress has been achieved in this interdisciplinary research area, the vastmajority of work has treated MHD as a binary classification task. Themulticlass classification setup is, however, essential if we are to uncover thesubtle differences among the statistical patterns of language use associatedwith particular mental health conditions. Here, we report on experiments aimedat predicting six conditions (anxiety, attention deficit hyperactivitydisorder, bipolar disorder, post-traumatic stress disorder, depression, andpsychological stress) from Reddit social media posts. We explore and comparethe performance of hybrid and ensemble models leveraging transformer-basedarchitectures (BERT and RoBERTa) and BiLSTM neural networks trained onwithin-text distributions of a diverse set of linguistic features. This setencompasses measures of syntactic complexity, lexical sophistication anddiversity, readability, and register-specific ngram frequencies, as well assentiment and emotion lexicons. In addition, we conduct feature ablationexperiments to investigate which types of features are most indicative ofparticular mental health conditions.

Exploring Hybrid and Ensemble Models for Multiclass Prediction of Mental Health Status on Social Media