Graduation Year
2024
Document Type
Thesis
Degree
M.S.C.S.
Degree Name
MS in Computer Science (M.S.C.S.)
Degree Granting Department
Computer Science and Engineering
Major Professor
Seungbae Kim, Ph.D.
Committee Member
Shaun Canavan, Ph.D.
Committee Member
John Templeton, Ph.D.
Abstract
Suicide prevention through early detection using social media data has been widely studied. However, the critical role of peer support interactions among individuals with similar mental disorders has not been deeply investigated or explored. In this study, we explore peer interactions in online communities for individuals with bipolar disorder and leverage this information to predict suicide risk levels. We propose a model that uses contextualized posts and comments along with their sentiment features. By embedding these features into a peer support network, our model captures peer interactions and predicts suicide risk levels using the bidirectional LSTM Graph Neural Networks (Bi-LSTM GNNs). Experimental results demonstrate the effectiveness of our approach, outperforming baseline methods. Our findings highlight the significant role of peer comments in predicting suicide risk in online communities.
Scholar Commons Citation
Marampelly, Harikrishna, "Learning Peer Support Interactions via Bi-LSTM Graph Neural Networks for Suicide Risk Prediction" (2024). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/10647
