Embedding Mental Health Discourse for Community Recommendation (2023)

Abstract: 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.

## introduction the rise of social media as a platform has allowed people all over the world to connect and communicate with one another. further, these communities that exist online are able to keep their members anonymous from one another, allowing new communities to form which would have a hard time existing without anonymity. specifically, this new and robust anonymity has allowed an explosion of online communities with a focus on giving each other advice on health issues. while being involved in seeking peer support in a community with people that have experienced similar issues can provide a significant positive impact on someone's ability to navigate their personal problems @xcite , finding communities with relevant discourse is not trivial. often, the platforms which host these communities have a * these authors contributed equally to this work very large quantity of them. there are over 100,000 different communities on reddit alone. further, some communities are not easily found due to their inherently anonymous nature, so the only way a user can decide if they fit within the community is by spending time reading through the discourse happening within the community. for these reasons, new users seeking others who have experienced similar situations may have a very hard time finding communities that would help them the most, even if they are familiar with the platform which hosts the communities. recently, embedding long sequences of text has received lots of interest both from the research community and from practitioners. a number of studies have shown embeddings can be useful for measuring the similarity both between document pairs and between question-document pairs @xcite @xcite , allowing for retrieval of the most similar documents given a new question or document. however, little work has been done investigating how the discourse within a community, which represents the meaning of that community, can be represented in a single embedding. the discourse of a community in this context can be all users' posts in that specific community or represented community's description. this poses a unique challenge as discourse within these communities is often in the form of threads that, unlike documents, are not naturally represented as a single block of text. the goal of this work is to develop a system to recommend support groups to social media users who seek help regarding mental health issues using embeddings to represent the communities and their discourse. specifically, we aim to leverage the text of a given user's posts along with the description and posts in each subreddit community to help recommend support groups that the user could consider joining. our main research questions are as follows: ## related work there are a number of studies related to our work. @xcite and @xcite constructed discourse embeddings to find relations between short text segments. while the two studies were similar in concept, they focused on short text segments where this work instead focused on constructing discourse embeddings for entire social media communities. @xcite showed nlp techniques could be used with electronic health records to predict mental health crises 4 weeks in advance. while online communities were no replacement for professional medical help, this suggested many who had looming mental health problems seek help before a crisis. low et al. (2020) experimented on the same dataset we used with natural language processing techniques such as tf-idf and sentiment analysis to understand the effects of covid-19 on mental health. although working on the same dataset, our work studies a different task: to recommend mental health-related support community to reddit users. @xcite adopted a similar approach to ours in content-based filtering for recommendation. specifically, they mapped a wikipedia page to each item and generate its corresponding vector representation using three feature-extraction methods -latent semantic indexing, random indexing, and word2vec. we extended this method by exploring more recent representations of text such as bert @xcite and openai embeddings. @xcite recommended threads in health forums based on the topics of interest of the users. specifically, self-reported medical conditions and symptoms of treatments were used as additional information to help improve thread recommendations @xcite . while our work is also situated in the health domain, we are interested in recommending a broader support group to users rather than a specific thread. ghazarian et al. (2022) used sentiment and other features to automatically evaluate dialog, showing nlp techniques could be used to evaluate quality of discourse. in doing so, they leveraged weak supervision to train a model on a large dataset without needing quality annotations. ## problem definition suppose we have a reddit's "who-posts-to-what" graph, which is denoted by @xmath0, v, e) where u is the set of users, v is the set of subreddit communities, and e, a subset of u × v , is the set of edges. the number of user nodes is @xmath1. so, @xmath2, p 1 ), (u 2 , p 2 ), ..., (u m , p m )} where p i is the set of posts by user u i and @xmath3, p ′ 1 ), ..., (v n , p ′ n )} where p ′ j is the set of all posts in subreddit v j . if a user u i posts to subreddit v j , there is an edge that goes from u i to v j , which is denoted by e @xmath4, v j ). the problem is that given g, predict if e @xmath5, v j ) exists. in other words, will user u i post to subreddit v j ? ## data and experimental setup for the experimental setup, we use the data from low et al. (2020) working on reddit platforms in mental health domains, particularly health anxiety. ## conclusion this study aimed to investigate the effectiveness of different types of discourse embeddings when integrated into content-based filtering for recommending support groups, particularly in the mental health domain. our findings showed that the hybrid model, which combined content-based filtering and collaborative filtering, yielded the best results. moreover, we conducted an extensive case study to demonstrate the interpretability of our approach's predictions. previous studies have brought to light the use of past behaviors to make more accurate recommendations in mental health @xcite . they also emphasize effective communication between the recommender system and the user as an essential factor for users' proper understanding of mental health in general as well as in their own journey @xcite . through promising prediction accuracy and interpretability, we believe that this method can serve as a valuable tool to support individuals, particularly those with mental health concerns, to share and seek help regarding their issues.