Poster: Extracting and Annotating Mental Health Forum Corpus: A Comprehensive Validation Pipeline - Jonnalagadda, Rohith Sundar; Azmee, Abm Adnan; Attota, Dinesh; Al Hafiz Khan, Md Abdullah; Pei, Yong; Nandan, Monica (IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (Online), 2024)
This research emphasizes the essential role of mental health forums as vital online communities providing solace, support, and resources for individuals grappling with mental health issues, especially among young people. Acknowledging the presence of severe content in some posts, indicative of acute distress and potential self-harm risk, the study draws on prior research highlighting the forums' critical role in fulfilling lower-level support needs for young individuals. By employing advanced classification and summarization techniques, namely Long short-term memory (LSTM), Bidirectional LSTM (BiLSTM) and BERT, the project aims to enhance the efficiency of these forums through systematic categorization and summarization of user posts. Preliminary results show promising outcomes, with improved post-classification accuracy ranging from 40% to 83.33% and average Rouge F1 scores ranging from 43% to 54%. This research contributes to fortifying the role of mental health forums in providing essential support to young individuals in distress.