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http://dx.doi.org/10.3745/KTSDE.2022.11.9.381

Understanding the Categories and Characteristics of Depressive Moods in Chatbot Data  

Chin, HyoJin (기초과학연구원 데이터사이언스그룹)
Jung, Chani (KAIST 전산학부)
Baek, Gumhee (이화여자대학교 간호대학)
Cha, Chiyoung (이화여자대학교 간호대학)
Choi, Jeonghoi (심심이 주식회사)
Cha, Meeyoung (KAIST 전산학부)
Publication Information
KIPS Transactions on Software and Data Engineering / v.11, no.9, 2022 , pp. 381-390 More about this Journal
Abstract
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.
Keywords
Chatbot; Depressive Discourse; Depressive Moods; Mental Health;
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Times Cited By KSCI : 1  (Citation Analysis)
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