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Research community has witnessed substantial growth in the detection ofmental health issues and their associated reasons from analysis of socialmedia. We introduce a new dataset for Causal Analysis of Mental health issuesin Social media posts (CAMS). Our contributions for causal analysis aretwo-fold: causal interpretation and causal categorization. We introduce anannotation schema for this task of causal analysis. We demonstrate the efficacyof our schema on two different datasets: (i) crawling and annotating 3155Reddit posts and (ii) re-annotating the publicly available SDCNL dataset of1896 instances for interpretable causal analysis. We further combine these intothe CAMS dataset and make this resource publicly available along withassociated source code: https://github.com/drmuskangarg/CAMS. We presentexperimental results of models learned from CAMS dataset and demonstrate that aclassic Logistic Regression model outperforms the next best (CNN-LSTM) model by4.9\% accuracy.

CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts