English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

As the popularity of social media platforms continues to rise, anever-increasing amount of human communication and self- expression takes placeonline. Most recent research has focused on mining social media for public useropinion about external entities such as product reviews or sentiment towardspolitical news. However, less attention has been paid to analyzing users'internalized thoughts and emotions from a mental health perspective. In thispaper, we quantify the semantic difference between public Tweets and privatemental health journals used in online cognitive behavioral therapy. We will usedeep transfer learning techniques for analyzing the semantic gap between thetwo domains. We show that for the task of emotional valence prediction, socialmedia can be successfully harnessed to create more accurate, robust, andpersonalized mental health models. Our results suggest that the semantic gapbetween public and private self-expression is small, and that utilizing theabundance of available social media is one way to overcome the small samplesizes of mental health data, which are commonly limited by availability andprivacy concerns.

Hashtag Healthcare: From Tweets to Mental Health Journals Using Deep Transfer Learning