A Causally Informed CNN-Ensemble Fusion Framework for Reddit-Based Mental Health Using Textual, Temporal, and Metadata Features

Abstract

Social media offers large-scale textual data on social indicators of distress, support-seeking and disclosure of psychological crises. Nevertheless, most mental health classification systems remain prediction focused and offer little information about how textual, temporal, and metadata signals interact to predict mental health. This paper presents an integrated causally informed convolutional neural network (CNN) ensemble fusion model for mental health risk classification using Reddit data. This study employs a post level Reddit mental health dataset with 159 CSV files. The dataset consisted of 50,000 posts selected from a balanced randomized sample of posts, with 10,000 posts from each of five classes. The framework includes three components: a CNN ensemble with a variety of convolution kernel sizes to capture phrase-level textual patterns; a structural causal model (SCM) guided metadata model, which formalizes assumptions related to the causal relationship between late-night posting, text length, engagement score, and high-risk subreddit category; and a fusion classifier that combines CNN probabilities with SCM adjusted risk estimates and scaled metadata covariates. Results demonstrate that the proposed SCM-guided fusion model can yield high-risk detection performance competitive with the best lexical baseline model, with regard to accuracy, as it approaches the best macro-F1, ROC-AUC and PR-AUC performance, and outperforms all other models in the multiclass setting. The framework provides an explainable, repeatable and ethically sensitive strategy for incorporating deep learning and causal inference in mental health research.