본문 바로가기 주메뉴 바로가기
22대 대한민국 국회 국회정보길라잡이 국회의원검색
회원가입 로그인
HOME

정보검색

소장정보 검색

국회도서관 홈으로 정보검색 소장정보 검색
결과 내 검색 동의어 포함
결과 내 검색 동의어 포함

-

초록보기

자연어처리 기술과 비대면 문화의 확산과 더불어 챗봇의 사용 증가세가 가파르며, 챗봇의 용도 또한 일상 대화와 소비자 응대를 넘어서 정신건강을 위한 용도로 확장하고 있다. 챗봇은 익명성이 보장된다는 점에서 사용자들이 우울감에 관해 이야기하기 적합한 서비스이다. 그러나 사용자가 작성한 문장들을 분석해 우울 담론의 유형과 특성을 파악하는 연구들은 주로 소셜 네트워크 데이터를 대상으로 했다는 한계점이 존재하며, 실제 환경에서 사용되는 챗봇과 상호작용한 데이터를 분석한 연구는 찾아보기 힘들다. 이 연구에서는 챗봇-사람의 상호작용 데이터에서 무작위로 추출한 ‘우울’과 관련된 대화 데이터를 토픽 모델링 방법과 텍스트마이닝 기법으로 분석하여 채팅에서의 우울 관련 담론의 특성을 파악하였다. 또한, 챗봇에서 빈번히 나타나는 ‘우울’ 담론의 범주와 트위터 ‘우울’ 담론의 범주의 차이점을 비교하였다. 이를 통해 챗봇 데이터의 ‘우울’ 대화만의 특징을 파악하고, 적절한 심리지원 정보를 제공하는 챗봇 서비스를 위한 시사점과 향후 연구 방향에 대해 논의한다.

Influenced by a culture that prefers non-face-to-face activity during the COVID-19 pandemic, chatbot usage is accelerating. Chatbots have been used for various purposes, not only for customer service in businesses and social conversations for fun but also for mental health. Chatbots are a platform where users can easily talk about their depressed moods because anonymity is guaranteed. However, most relevant research has been on social media data, especially Twitter data, and few studies have analyzed the commercially used chatbots data. In this study, we identified the characteristics of depressive discourse in user-chatbot interaction data by analyzing the chats, including the word ‘depress,’ using the topic modeling algorithm and the text-mining technique. Moreover, we compared its characteristics with those of the depressive moods in the Twitter data. Finally, we draw several design guidelines and suggest avenues for future research based on the study findings.

권호기사

권호기사 목록 테이블로 기사명, 저자명, 페이지, 원문, 기사목차 순으로 되어있습니다.
기사명 저자명 페이지 원문 목차
스마트팜을 위한 웹 기반 데이터 분석 서비스 = Web-based data analysis service for smart farms 정지민, 이지현, 노혜민 p. 355-362

RGB-D 환경인식 시각 지능, 목표 사물 경로 탐색 및 심층 강화학습에 기반한 사람형 로봇손의 목표 사물 파지 = Grasping a target object in clutter with an anthropomorphic robot hand via RGB-D vision intelligence, target path planning and deep reinforcement learning 류가현, 오지헌, 정진균, 정환석, 이진혁, Patricio Rivera Lopez, 김태성 p. 363-370

결합된 파라메트릭 활성함수를 이용한 합성곱 신경망의 성능 향상 = Performance improvement method of convolutional neural network using combined parametric activation functions 고영민, 이붕항, 고선우 p. 371-380

챗봇 데이터에 나타난 우울 담론의 범주와 특성의 이해 = Understanding the categories and characteristics of depressive moods in chatbot data 진효진, 정찬이, 백금희, 차지영, 최정회, 차미영 p. 381-390

학습자의 주의집중을 유도하는 모바일 애플리케이션 프로토타입 개발 = Development of mobile application prototype inducing learner’s attention 노경의, 이찬행, 박지수, 손진곤 p. 391-398

참고문헌 (36건) : 자료제공( 네이버학술정보 )

참고문헌 목록에 대한 테이블로 번호, 참고문헌, 국회도서관 소장유무로 구성되어 있습니다.
번호 참고문헌 국회도서관 소장유무
1 G. Dosovitsky, B. S. Pineda, N. C. Jacobson, C. Chang, and E. L. Bunge, “Artificial intelligence chatbot for depression:Descriptive study of usage,” JMIR Formative Research, Vol.4, No.11, pp.e17065, 2020. 미소장
2 G. M. Lucas, A. Rizzo, J. Gratch, S. Scherer, G. Stratou, J. Boberg, and L. P. Morency, “Reporting mental health symptoms: Breaking down barriers to care with virtual human interviewers,” Frontiers in Robotics and AI, Vol.4, No.51, pp.1-9, 2017. 미소장
3 M. C. Klos, M. Escoredo, A. Joerin, V. N. Lemos, M. Rauws, and E. L. Bunge, “Artificial intelligence–based chatbot for anxiety and depression in university students: Pilot randomized controlled trial,” JMIR Formative Research, Vol.5, No.8, pp.e20678, 2021. 미소장
4 B. Reeves and C. I. Nass, “The media equation: How people treat computers, television, and new media like real people and places,” Cambridge University Press, 1996. 미소장
5 M. Park, C. Cha, and M. Cha, “Depressive moods of users portrayed in Twitter,” In Proceedings of the ACM SIGKDD Workshop on Health Informatics, Beijing, China, pp.1-8, 2012. 미소장
6 E. M. Lachmar, A. K. Wittenborn, K. W. Bogen, and H. L. McCauley, “#MyDepressionLooksLike: Examining public discourse about depression on Twitter,” JMIR Mental Health, Vol.4, No.4, pp.e43, 2017. 미소장
7 M. Kumar, M. Dredze, G. Coppersmith, and M. De Choudhury, “Detecting changes in suicide content manifested in social media following celebrity suicides,” In Proceedings of the 26th ACM Conference on Hypertext & Social Media, pp.85-94, 2015. 미소장
8 D. Mowery, H. Smith, T. Cheney, G. Stoddard, G. Coppersmith, C., Bryan, and M. Conway, “Understanding depressive symptoms and psychosocial stressors on Twitter: a corpusbased study,” Journal of Medical Internet Research, Vol.19, No.2, pp.e6895, 2017. 미소장
9 M. Lee, S. Ackermans, N. Van As, H. Chang, E. Lucas, and W. IJsselsteijn, “Caring for vincent: A chatbot for selfcompassion,”In Proceedings of the CHI Conference on Human Factors in Computing Systems (ACM CHI), Glasgow, Scotland, UK., pp.1-13, 2019. 미소장
10 Y. C. Lee, N. Yamashita, Y. Huang, and W. Fu, “I Hear You, I Feel You: Encouraging deep self-disclosure through a chatbot,” In Proceedings of the CHI Conference on Human Factors in Computing Systems (ACM CHI), Honolulu, HI, USA, pp.1-12, 2020. 미소장
11 J. Posner, J. A. Russell, and B. S. Peterson, “The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology,”Development and Psychopathology, Vol.17, No.3, pp.715-34, 2005. 미소장
12 M. De Choudhury, S. S. Sharma, T. Logar, W. Eekhout, and R. C. Nielsen, “Gender and cross-cultural differences in social media disclosures of mental illness,” Proceedings of the 2017ACM Conference on Computer Supported Cooperative Work and Social Computing, p.353-369, 2017. 미소장
13 S. R. Pendse, K. Niederhoffer, and A. Sharma, “Cross-cultural differences in the use of online mental health support forums,” Proceedings of the ACM on Human-Computer Interaction, CSCW, pp.1-29, 2019. 미소장
14 K. C. Bathina, M. Ten Thij, L. Lorenzo-Luaces, L. A. Rutter, and J. Bollen, “Individuals with depression express more distorted thinking on social media,” Nature Human Behaviour, Vol.5, No.4, pp.458-466, 2021. 미소장
15 J. Brailovskaia and J. Margraf, “What does media use reveal about personality and mental health? An exploratory investigation among German students,” PloS one, Vol.13, No.1, pp.e0191810, 2019. 미소장
16 B. J. Bushman, R. F. Baumeister, and C. M. Phillips, “Do people aggress to improve their mood? Catharsis beliefs, affect regulation opportunity, and aggressive responding,” Journal of Personality and Social Psychology, Vol.81, No.1, pp.17-32, 2001. 미소장
17 C. P. Kimball, “The healer within: The new medicine of mind and body,” JAMA, Vol.256, No.23, pp.3290-3290, 1996. 미소장
18 N. Andalibi, P. Ozturk, and A. Forte, “Sensitive self-disclosures, responses, and social support on instagram: The case of# depression,” Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, pp.1485-1500, 2017. 미소장
19 M. Paul and M. Dredze, “You are what you tweet: Analyzing twitter for public health,” Proceedings of the International AAAI Conference on Web and Social Media, Vol.5, No.1, 2011. 미소장
20 M. W. Newman, D. Lauterbach, S. A., Munson, P. Resnick, and M. E. Morris, “It's not that I don't have problems, I'm just not putting them on Facebook: Challenges and opportunities in using online social networks for health,”In Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work, pp.341-350, 2011. 미소장
21 A. Kramer and C. Chung, “Dimensions of self-expression in Facebook status updates,” Proceedings of the International AAAI Conference on Web and Social Media, Vol.5, No.1, pp.169-176, 2011. 미소장
22 M. De Choudhury, and S. De, “Mental health discourse on reddit: Self-disclosure, social support, and anonymity,”Eighth International AAAI Conference on Weblogs and Social Media, 2014. 미소장
23 R. Skaik and D. Inkpen, “Using social media for mental health surveillance: A review,” ACM Computing Surveys (CSUR), Vol.53, No.6, pp.1-31, 2020. 미소장
24 G. Dosovitsky, E. Kim, and E. L. Bunge, “Psychometric properties of a chatbot version of the PHQ-9 with adults and older adults,” Frontiers in Digital Health, Vol.3, pp.41, 2021. 미소장
25 M. Conway, “Ethical issues in using Twitter for public health surveillance and research: Developing a taxonomy of ethical concepts from the research literature,” Journal of Medical Internet Research, Vol.16, No.12, pp.e290, 2014. 미소장
26 D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” Journal of Machine Learning Research, Vol.3, No.Jan., pp.993-1022, 2003. 미소장
27 Y. R. Suh, K. P. Koh, and J. Lee, “An analysis of the change in media's reports and attitudes about face masks during the COVID-19 pandemic in South Korea: A study using Big Data latent dirichlet allocation (LDA) topic modelling,”Journal of the Korea Institute of Information and Communication Engineering, Vol.25, No.5, pp.731-740, 2021. 미소장
28 E. L. Park and S. Cho, “KoNLPy: Korean natural language processing in Python,” Annual Conference on Human and Language Technology, Human and Language Technology, pp.133-136, 2014. 미소장
29 A. Shrestha and F. Spezzano, “Detecting depressed users in online forums,” In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Vancouver, Canada, pp.945-951, 2019. 미소장
30 M. De Choudhury, S. Counts, E. J. Horvitz, and A. Hoff, “Characterizing and predicting postpartum depression from shared facebook data,” In Proceedings of the ACM Conference on Computer Supported Cooperative Work & Social Computing (ACM CSCW), Baltimore, Maryland, USA, pp.626-638, 2014. 미소장
31 H. J. Jeon et al., “Melancholic features and hostility are associated with suicidality risk in Asian patients with major depressive disorder,” Journal of Affective Disorders, Vol.148, No.2-3, pp.368-374, 2013. 미소장
32 S. Park et al., “The association of suicide risk with negative life events and social support according to gender in Asian patients with major depressive disorder,” Psychiatry Research, Vol.228, No.3, pp.277-282, 2015. 미소장
33 X. Ma, J. Hancock, and M. Naama, “Anonymity, intimacy and self-disclosure in social media,” In Proceedings of the CHI Conference on Human Factors in Computing Systems (ACM CHI), San Jose, CA, USA, pp.3857-3869, 2016. 미소장
34 R. Zhang, “The stress-buffering effect of self-disclosure on Facebook: An examination of stressful life events, social support, and mental health among college students,” Computers in Human Behavior, Vol.75, pp.527-537, 2017. 미소장
35 H. J. Jeon et al., “Differences in depressive symptoms between Korean and American outpatients with major depressive disorder,” International Clinical Psychopharmacology, Vol.29, No.3, pp.150-156, 2014. 미소장
36 H. Chin and M. Y. Yi, “Voices that care differently: Understanding the effectiveness of a conversational agent with an alternative empathy orientation and emotional expressivity in mitigating verbal abuse,” International Journal of Human-Computer Interaction, Vol.38, No.12, pp.1153-1167, 2022. 미소장

추천서가 (다양한 추천 자료를 만나보세요)

권호기사보기

권호기사 목록 테이블로 기사명, 저자명, 페이지, 원문, 기사목차 순으로 되어있습니다.
기사명 저자명 페이지 원문 기사목차