Embedding Mental Health Discourse for Community Recommendation
Hy Dang Bang Nguyen Noah Ziems Meng Jiang
Our paper investigates the use of discourse embedding techniques to develop a
community recommendation system that focuses on mental health support groups on
social media. Social media platforms provide a means for users to anonymously
connect with communities that cater to their specific interests. However, with
the vast number of online communities available, users may face difficulties in
identifying relevant groups to address their mental health concerns. To address
this challenge, we explore the integration of discourse information from
various subreddit communities using embedding techniques to develop an
effective recommendation system. Our approach involves the use of content-based
and collaborative filtering techniques to enhance the performance of the
recommendation system. Our findings indicate that the proposed approach
outperforms the use of each technique separately and provides interpretability
in the recommendation process.