Analyzing Prediction of Depression and Anxiety on Reddit: a Multi-task Learning Approach through GMMTL

Abstract One of the strongest indicators of a mental health crisis is how people interact with each other or express themselves. Hence, social media is an ideal source to extract user-level information about the language used to express personal feelings. In the wake of the ever-increasing mental health crisis in the United States, it is imperative to analyze the general well-being of a population and investigate how their public social media posts can be used to detect different underlying mental health conditions. For that purpose, we propose a study that collects posts from "reddits" related to different mental health topics to detect the type of the post and the nature of the mental health issues that correlate to the post. The task of detecting mental health related issues indicates the mental health conditions connected to the posts.However, it is also important to be able to detect unnecessary post that are directly talking about the topic of interest. To achieve this, we expand our multi-task learning model that leverages, for each post, both the latent embedding space of words and topics for prediction with a message passing mechanism enabling the sharing of information for related tasks. We train the model through an active learning approach in order to tackle the lack of standardized fine-grained label data for this specific task. We further add control group in the dataset to detect non-mentalhealth related post. Finally, we conduct a case study to show what are the important features in different mental health condition predictions.