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Applied Graph Convolutional Networks for Identifying Individuals with ASD Experiencing Mental Health Crises (DSI-SRP)

Posted by on Sunday, August 15, 2021 in College of Arts and Science, Completed Research, DSI-SRP, DSI-Supported Research, Engineering, School of Engineering, Social and Behavioral Sciences.

This DSI-SRP fellowship funded Benjamin Van Sleen to work in the Network and Data Science (NDS) laboratory led by Assistant Professor Tyler Derr in the Department of Computer Science during the summer of 2021. Benjamin is a junior with majors in Computer Engineering and Economics.

The project funded by this fellowship aimed to understand relationships between autism spectrum disorder and severe mental illnesses like depression through studying interactions within specific communities in online social networks. The custom dataset consisted of posts and comments collected from specific threads and users on Reddit. Using existing natural language processing (NLP) models, graphs (defined as a collection of “nodes” connected by “edges”) were constructed modelling both the number of and relative sentiment (whether a message was positive or negative, kind or toxic, etc.) of interactions between users in the form of a social network. For example, in such a graph, the users would be the nodes and they would be connected by edges representing the interactions between the users.

By analyzing various directed graphs composed of users from specific communities (such as a graph modelling interactions between frequent posters in the subreddits r/Autism and r/depression), graph statistics like reciprocity (the likelihood of a connection/comment from User A to User B existing when given a connection/comment already exists from User B to User A) and the distribution of node degrees (the total number of connections a user has) provided insight on the frequency and types of communication between communities of users. By incorporating the sentiment of interactions between users, signed network analysis (where the social networks contain both positive and negative relations) can be performed. More specifically, social triads can be examined according to the sentiment associated with the three relations connecting the three connected users, which gives insight into whether the distribution of these triads adhere to those commonly found in other social networks (based on existing social science theories, such as social balance theory, which categories triads as either balanced/stable or unbalanced/unstable and likely to deteriorate into the stabler forms). As a proof of concept, a Graph Neural Network (GNN) was built (in collaboration with his partner Chet Weissberg) to predict the subreddit a post came from when masking that information fed into the GNN prediction model. GNN’s mostly make predictions or classifications for each node/user or for the graph as a whole. By considering each post and its comment tree as a graph containing information about each post’s contents and each post’s author’s previous interactions, the model classified which subreddit the post and its associated comments were most likely to have come from. Ben hopes to refine the design of the data and develop a novel tree-based temporal GNN that takes as input a given user’s history of posts and interactions with other users to create a system that has the ability to then output/predict which mental health related communities (e.g., r/SuicideWatch) a user is likely going to start posting in within the near future. Note that such a could be leveraged for early suicide ideation detection.

In addition to receiving support through a DSI-SRP fellowship, this project was supported and facilitated by the DSI Data Science Team through their regular summer workshops and demo sessions.

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