Embedding Mental Health Discourse for Community Recommendation
This addresses the challenge of identifying appropriate online communities for mental health support, though it appears incremental in combining existing methods.
The paper tackles the problem of helping users find relevant mental health support groups on social media by developing a recommendation system that integrates discourse embedding techniques with content-based and collaborative filtering, resulting in improved performance over using each technique separately.
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.