  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.
