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  1. NTU Theses and Dissertations Repository
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Please use this identifier to cite or link to this item: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81911
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???org.dspace.app.webui.jsptag.ItemTag.dcfield???ValueLanguage
dc.contributor.advisor曹承礎(Seng-Cho Chou)
dc.contributor.authorKuo-Wei Tsengen
dc.contributor.author曾國瑋zh_TW
dc.date.accessioned2022-11-25T03:06:26Z-
dc.date.available2023-11-01
dc.date.copyright2021-11-05
dc.date.issued2021
dc.date.submitted2021-10-25
dc.identifier.citation1. Denny, B.T. and K.N. Ochsner, Behavioral effects of longitudinal training in cognitive reappraisal. Emotion, 2014. 14(2): p. 425-433. 2. Zhang, C.Q., et al., Occupational stressors, mental health, and sleep difficulty among nurses during the COVID-19 pandemic: The mediating roles of cognitive fusion and cognitive reappraisal. J Contextual Behav Sci, 2021. 19: p. 64-71. 3. Blechert, J., et al., See What You Think:Reappraisal Modulates Behavioral and Neural Responses to Social Stimuli. Psychological Science, 2012. 23(4): p. 346-353. 4. Doré, B.P., et al., Helping Others Regulate Emotion Predicts Increased Regulation of One's Own Emotions and Decreased Symptoms of Depression. Personality amp; social psychology bulletin, 2017. 43(5): p. 729-739. 5. Morris, R. and R. Picard, Crowdsourcing Collective Emotional Intelligence. 2012. 6. Naslund, J.A., et al., The future of mental health care: peer-to-peer support and social media. Epidemiology and Psychiatric Sciences, 2016. 25(2): p. 113-122. 7. Peng, Z., et al., Exploring the Effects of Technological Writing Assistance for Support Providers in Online Mental Health Community, in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 2020, Association for Computing Machinery: Honolulu, HI, USA. p. 1–15. 8. Cohen, N. and K.N. Ochsner, From surviving to thriving in the face of threats: the emerging science of emotion regulation training. Current Opinion in Behavioral Sciences, 2018. 24: p. 143-155. 9. Coyle, S., et al., School-based treatment for children and adolescents with social anxiety disorder. 2020. p. 237-254. 10. O'Leary, K., et al., Design Opportunities for Mental Health Peer Support Technologies, in Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. 2017, Association for Computing Machinery: Portland, Oregon, USA. p. 1470–1484. 11. Wang, Y.-C., R. Kraut, and J. Levine, To Stay or Leave? The Relationship of Emotional and Informational Support to Commitment in Online Health Support Groups. 2012. 833-842. 12. Kim, T., M. Ruensuk, and H. Hong, In Helping a VulnerA/Ble Bot, You Help Yourself: Designing a Social Bot as a Care-Receiver to Promote Mental Health and Reduce Stigma, in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 2020, Association for Computing Machinery: Honolulu, HI, USA. p. 1–13. 13. Sharma, E. and M.D. Choudhury, Mental Health Support and its Relationship to Linguistic Accommodation in Online Communities, in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 2018, Association for Computing Machinery: Montreal QC, Canada. p. Paper 641. 14. Chancellor, S., Z. Lin, and M. Choudhury, 'This Post Will Just Get Taken Down': Characterizing Removed Pro-Eating Disorder Social Media Content. 2016. 1157-1162. 15. Dosono, B. and B. Semaan, Moderation Practices as Emotional LA/Bor in Sustaining Online Communities: The Case of AAPI Identity Work on Reddit, in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019, Association for Computing Machinery: Glasgow, Scotland Uk. p. Paper 142. 16. Lampel, J. and A. Bhalla, The Role of Status Seeking in Online Communities: Giving the Gift of Experience. Journal of Computer-Mediated Communication, 2007. 12(2): p. 434-455. 17. Moorhead, S.A., et al., A New Dimension of Health Care: Systematic Review of the Uses, Benefits, and Limitations of Social Media for Health Communication. J Med Internet Res, 2013. 15(4): p. e85. 18. Yang, D., et al., Seekers, Providers, Welcomers, and Storytellers: Modeling Social Roles in Online Health Communities, in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019, Association for Computing Machinery: Glasgow, Scotland Uk. p. Paper 344. 19. Geraedts, A., et al., Long-Term Results of a Web-Based Guided Self-Help Intervention for Employees With Depressive Symptoms: Randomized Controlled Trial. Journal of Medical Internet Research, 2014. 20. Lederman, R., et al., Moderated online social therapy: Designing and evaluating technology for mental health. ACM Trans. Comput.-Hum. Interact., 2014. 21(1): p. Article 5. 21. Morris, R.R., S.M. Schueller, and R.W. Picard, Efficacy of a Web-based, crowdsourced peer-to-peer cognitive reappraisal platform for depression: randomized controlled trial. J Med Internet Res, 2015. 17(3): p. e72. 22. Gero, K.I. and L.B. Chilton, Metaphoria: An Algorithmic Companion for Metaphor Creation, in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019, Association for Computing Machinery: Glasgow, Scotland Uk. p. Paper 296. 23. Kim, T., et al., Love in Lyrics: An Exploration of Supporting Textual Manifestation of Affection in Social Messaging. Proc. ACM Hum.-Comput. Interact., 2019. 3(CSCW): p. Article 79. 24. Harandi, T.F., M.M. TaghinasA/B, and T.D. Nayeri, The correlation of social support with mental health: A meta-analysis. Electronic physician, 2017. 9(9): p. 5212-5222. 25. Sahi, R.S., E. Ninova, and J.A. Silvers, With a little help from my friends: Selective social potentiation of emotion regulation. Journal of Experimental Psychology: General, 2020: p. No Pagination Specified-No Pagination Specified. 26. Strauss, G.P., K.L. Ossenfort, and K.M. Whearty, Reappraisal and distraction emotion regulation strategies are associated with distinct patterns of visual attention and differing levels of cognitive demand. PLoS ONE, 2016. 11(11). 27. Schimel, J., et al., Running from the shadow: psychological distancing from others to deny characteristics people fear in themselves. J Pers Soc Psychol, 2000. 78(3): p. 446-62. 28. Orvell, A., et al., Linguistic Shifts: A Relatively Effortless Route to Emotion Regulation? Current Directions in Psychological Science, 2019. 28(6): p. 567-573. 29. Christensen, K.A., et al., Evaluating interactions between emotion regulation strategies through the interpersonal context of female friends. Journal of Clinical Psychology. n/a(n/a). 30. Niven, K., et al., Emotion Regulation of Others and Self (EROS): The Development and Validation of a New Individual Difference Measure. Current Psychology, 2011. 30: p. 53-73. 31. Darcy, A., et al., Evidence of Human-Level Bonds EstA/Blished With a Digital Conversational Agent: Cross-sectional, Retrospective Observational Study. JMIR Form Res, 2021. 5(5): p. e27868. 32. O'Leary, K., et al., “Suddenly, we got to become therapists for each other”: Designing Peer Support Chats for Mental Health, in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 2018, Association for Computing Machinery: Montreal QC, Canada. p. Paper 331. 33. Kim, T., M. Ruensuk, and H. Hong, In Helping a VulnerA/Ble Bot, You Help Yourself: Designing a Social Bot as a Care-Receiver to Promote Mental Health and Reduce Stigma. 2020. 1-13. 34. Ng, Y.K. Research Paper Recommendation Based on Content Similarity, Peer Reviews, Authority, and Popularity. in 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI). 2020. 35. Aciar, S.G., Aciar, Peer recommendation based on comments write on social networks, in XX Congreso Argentino de Ciencias de la Computación. 2014. 36. Sie, R., et al., To whom and why should I connect? Co-author recommendation based on powerful and similar peers. International Journal of Technology Enhanced Learning, 2012. 4: p. 121-137. 37. Hui, J.S., D. Gergle, and E.M. Gerber, IntroAssist: A Tool to Support Writing Introductory Help Requests, in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 2018, Association for Computing Machinery: Montreal QC, Canada. p. Paper 22. 38. Spitzer, R.L., et al., A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med, 2006. 166(10): p. 1092-7. 39. Schaefer, B., et al., Emerging Processes Within Peer-Support Hearing Voices Groups: A Qualitative Study in the Dutch Context. Frontiers in Psychiatry, 2021. 12(494). 40. Mead, S., D. Hilton, and L. Curtis, Peer support: a theoretical perspective. Psychiatr RehA/Bil J, 2001. 25(2): p. 134-41. 41. Paloniemi, E., et al., Measures of empathy and the capacity for self-reflection in dental and medical students. BMC Medical Education, 2021. 21(1): p. 114. 42. Grant, A., J. Franklin, and P. Langford, The Self-Reflection and Insight Scale: A New Measure of Private Self-Consciousness. Social Behavior and Personality: an international journal, 2002. 30: p. 821-835.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81911-
dc.description.abstract認知再評估(reappraisal)是一項有助人類負面情緒與想法調節的關鍵策略,不過,當面對具有壓力的狀況時,要自行靈活應用、實現認知再評估通常並不容易,因此,線上心理健康社區(online mental health communities,OMHCs)成為了新興趨勢,供人們尋求及提供幫助。 然而,現存研究發現, OMHCs提供支持觀點的內容品質低落,大多採用事後才篩除不適言論或事前訓練支持提供者(support provider)來解決此問題,此兩種解決方案皆存在效率不彰的問題,也會拉低平台使用的留存率。 本研究提出AI驅動心理健康互助平台:VoissBot,搭載媒合參考答案之功能,在使用者提供互助內容後即時分析用戶語意並給予適配的參考答案,用以達到長期、潛在提升支持提供者幫助他人進行認知再評估的能力。正式實驗中,受測者分為A、B組,分別媒合相似與歧異觀點,透過比較兩組的支持品質,得出較好的系統媒合機制。 我們邀請四位心理專家訂定準則,首先對實驗前置準備數據進行人工標記,並定義出關鍵七大分類標籤,接著本研究基於此結果訓練出認知再評估多標籤分類器,準確率達89%。 正式實驗階段,本研究套用上述分類器來比較A、B兩組結果後,發現B組給予歧異觀點的自動化媒合機制,相較於A組,更能在無須矯正支持提供者觀點的條件下潛在提升其支持品質(p<0.001),結合心理師專家歸納的觀點,我們進一步探究如何建置能真正促進用戶認知再評估的AI驅動心理健康互助平台。zh_TW
dc.description.provenanceMade available in DSpace on 2022-11-25T03:06:26Z (GMT). No. of bitstreams: 1
U0001-1110202109530800.pdf: 2072873 bytes, checksum: 1c4a0dfc8b7d9793af245729dc314050 (MD5)
Previous issue date: 2021
en
dc.description.tableofcontents第一章、緒論 1 1.1 研究背景 2 1.2 研究動機 2 1.3 研究目的 3 1.4 論文架構 4 第二章、文獻探討 5 2.1 認知再評估 5 2.2 線上同伴互助平台 6 2.3 支持內容品質評估 8 2.4 觀點相似的內容媒合系統 8 第三章、研究設計 10 3.1 研究問題 10 3.2 研究架構 11 3.3 研究驗證 11 3.3.1 專家人工標註 16 3.3.2 深度學習分類 21 3.4 研究流程 22 3.4.1 AI驅動心理健康互助平台設計 24 3.4.2 資料蒐集 30 第四章、研究結果 34 4.1 專家人工標註結果 34 4.2 深度學習分類結果 45 4.3 觀點相似度媒合系統結果分析 51 第五章、討論 53 第六章、結論 59 第七章、參考文獻 60
dc.language.isozh-TW
dc.subject社群網路zh_TW
dc.subject深度學習zh_TW
dc.subject心理健康zh_TW
dc.subject人工智慧媒合系統zh_TW
dc.subject自然語言處理zh_TW
dc.subjectNatural Language Processingen
dc.subjectSocial Networksen
dc.subjectArtificial Intelligence Matching Systemen
dc.subjectDeep Learningen
dc.subjectMental Healthen
dc.titleAI驅動之同伴支持媒合系統:促進線上心理健康社區之認知再評估zh_TW
dc.titleDesigning an AI-driven Peer Support Matching System to Facilitate Cognitive Reappraisal for Online Mental Health Communityen
dc.date.schoolyear109-2
dc.description.degree碩士
dc.contributor.oralexamcommittee盧信銘(Hsin-Tsai Liu),陳建錦(Chih-Yang Tseng)
dc.subject.keyword自然語言處理,人工智慧媒合系統,心理健康,深度學習,社群網路,zh_TW
dc.subject.keywordNatural Language Processing,Artificial Intelligence Matching System,Mental Health,Deep Learning,Social Networks,en
dc.relation.page62
dc.identifier.doi10.6342/NTU202103645
dc.rights.note同意授權(全球公開)
dc.date.accepted2021-10-26
dc.contributor.author-college管理學院zh_TW
dc.contributor.author-dept資訊管理學研究所zh_TW
dc.date.embargo-lift2023-11-01-
Appears in Collections:資訊管理學系

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