Poster: Extracting and Annotating Mental Health Forum Corpus: A Comprehensive Validation Pipeline
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...
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| Veröffentlicht in: | IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (Online) S. 208 - 209 |
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| Sprache: | Englisch |
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IEEE
19.06.2024
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| ISSN: | 2832-2975 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Al Hafiz Khan, Md Abdullah Azmee, Abm Adnan Nandan, Monica Attota, Dinesh Pei, Yong Jonnalagadda, Rohith Sundar |
| Author_xml | – sequence: 1 givenname: Rohith Sundar surname: Jonnalagadda fullname: Jonnalagadda, Rohith Sundar email: rjonnal1@students.kennesaw.edu organization: Department of Computer Science – sequence: 2 givenname: Abm Adnan surname: Azmee fullname: Azmee, Abm Adnan email: aazmee@students.kennesaw.edu organization: Department of Computer Science – sequence: 3 givenname: Dinesh surname: Attota fullname: Attota, Dinesh email: dattota@students.kennesaw.edu organization: Data Science and Analytics – sequence: 4 givenname: Md Abdullah surname: Al Hafiz Khan fullname: Al Hafiz Khan, Md Abdullah email: mkhan74@kennesaw.edu organization: Department of Computer Science – sequence: 5 givenname: Yong surname: Pei fullname: Pei, Yong email: ypei@kennesaw.edu organization: Department of Computer Science – sequence: 6 givenname: Monica surname: Nandan fullname: Nandan, Monica email: mnandan@kennesaw.edu organization: Social Work and Human Services Kennesaw State University,Marietta,USA |
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| Snippet | This research emphasizes the essential role of mental health forums as vital online communities providing solace, support, and resources for individuals... |
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| SubjectTerms | Accuracy Bidirectional control Distance measurement Encoding Mental health Mental health forums natural language processing online communities Pipelines Systematics text classification text summarization |
| Title | Poster: Extracting and Annotating Mental Health Forum Corpus: A Comprehensive Validation Pipeline |
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