A Prototypical Classifier with Boosting Augmented Redundancy Detector for Causal Analysis of Mental Health over Social Media

Online social media are frequently used by people as a way of expressing their thoughts and feelings. Among the vast amounts of online posts, there may be more concerning ones expressing potential grievances and mental illnesses. Identifying these along with potential causes of mental health problems is an important task. By observing posts on social media, we find that users have a tendency to publish long posts expressing negative emotions, yet may rarely articulate the causes of negative emotions. Therefore, we propose a novel prototype-based classifier with data augmentation through verbalization boosting to help the language model focus on potentially causative sentences. Extensive experiments validate the effectiveness of our model on the benchmark datasets Intent_SDCNL and SAD.