Hashtag Healthcare: From Tweets to Mental Health Journals Using Deep Transfer Learning
This addresses the challenge of small sample sizes in mental health data for researchers and practitioners, though it is incremental in applying transfer learning to a new domain.
The paper tackled the problem of analyzing users' internalized thoughts and emotions from a mental health perspective by quantifying the semantic difference between public Tweets and private mental health journals, showing that social media can be used to create more accurate, robust, and personalized mental health models for emotional valence prediction.
As the popularity of social media platforms continues to rise, an ever-increasing amount of human communication and self- expression takes place online. Most recent research has focused on mining social media for public user opinion about external entities such as product reviews or sentiment towards political news. However, less attention has been paid to analyzing users' internalized thoughts and emotions from a mental health perspective. In this paper, we quantify the semantic difference between public Tweets and private mental health journals used in online cognitive behavioral therapy. We will use deep transfer learning techniques for analyzing the semantic gap between the two domains. We show that for the task of emotional valence prediction, social media can be successfully harnessed to create more accurate, robust, and personalized mental health models. Our results suggest that the semantic gap between public and private self-expression is small, and that utilizing the abundance of available social media is one way to overcome the small sample sizes of mental health data, which are commonly limited by availability and privacy concerns.