LonXplain: Lonesomeness as a Consequence of Mental Disturbance in Reddit Posts

Social media is a potential source of information that infers latent mental states through Natural Language Processing (NLP). While narrating real-life experiences, social media users convey their feeling of loneliness or isolated lifestyle, impacting their mental well-being. Existing literature on psychological theories points to loneliness as the major consequence of interpersonal risk factors, propounding the need to investigate loneliness as a major aspect of mental disturbance. We formulate lonesomeness detection in social media posts as an explainable binary classification problem, discovering the users at-risk, suggesting the need of resilience for early control. To the best of our knowledge, there is no existing explainable dataset, i.e., one with human-readable, annotated text spans, to facilitate further research and development in loneliness detection causing mental disturbance. In this work, three experts: a senior clinical psychologist, a rehabilitation counselor, and a social NLP researcher define annotation schemes and perplexity guidelines to mark the presence or absence of lonesomeness, along with the marking of text-spans in original posts as explanation, in 3,521 Reddit posts. We expect the public release of our dataset, LonXplain, and traditional classifiers as baselines via GitHub

Analyzing Tweets For Predicting Mental Health States Using Data Mining And Machine Learning Algorithms

Tweets are usually the outcome of peoples’ feelings on various topics. Twitter allows users to post casual and emotional thoughts to share in real-time. Around 20% of U.S. adults use Twitter. Using the word-frequency and singular value decomposition methods, we identified the behavior of individuals through their tweets. We graded depressive and anti-depressive keywords using the tweet time-series, time-window, and time-stamp methods. We have collected around four million tweets since 2018. A parameter (Depressive Index) is computed using the F1 score and Mathews correlation coefficient (MCC) to indicate the depressive level. A framework showing the Depressive Index and the Happiness Index is prepared with the time, location, and keywords and delivers F1 Score, MCC, and CI values. COVID-19 changed the routines of most peoples\u27 lives and affected mental health. We studied the tweets and compared them with the COVID-19 growth. The Happiness Index from our work and World Happiness Report for Georgia, New York, and Sri Lanka is compared. An interactive framework is prepared to analyze the tweets, depict the happiness index, and compare it. Bad words in tweets are analyzed, and a map showing the Happiness Index is computed for all the US states and was compared with WalletHub data. We add tweets continuously and a framework delivering an atlas of maps based on the Happiness Index and make these maps available for further study. We forecasted tweets with real-time data. Our results of tweets and COVID-19 reports (WHO) are in a similar pattern. A new moving average method was presented; this unique process gave perfect results at peaks of the function and improved the error percentage. An interactive GUI portal computes the Happiness Index, depression index, feel-good- factors, prediction of the keywords, and prepares a Happiness Index map. We plan to create a public web portal to facilitate users to get these results. Upon completing the proposed GUI application, the users can get the Happiness Index, Depression Index values, Happiness map, and prediction of keywords of the desired dates and geographical locations instantaneously

GLP-1 Receptor Agonists and Related Mental Health Issues; Insights from a Range of Social Media Platforms Using a Mixed-Methods Approach

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/The emergence of glucagon-like peptide-1 receptor agonists (GLP-1 RAs; semaglutide and others) now promises effective, non-invasive treatment of obesity for individuals with and without diabetes. Social media platforms’ users started promoting semaglutide/Ozempic as a weight-loss treatment, and the associated increase in demand has contributed to an ongoing worldwide shortage of the drug associated with levels of non-prescribed semaglutide intake. Furthermore, recent reports emphasized some GLP-1 RA-associated risks of triggering depression and suicidal thoughts. Consistent with the above, we aimed to assess the possible impact of GLP-1 RAs on mental health as being perceived and discussed in popular open platforms with the help of a mixed-methods approach. Reddit posts yielded 12,136 comments, YouTube videos 14,515, and TikTok videos 17,059, respectively. Out of these posts/entries, most represented matches related to sleep-related issues, including insomnia (n = 620 matches); anxiety (n = 353); depression (n = 204); and mental health issues in general (n = 165). After the initiation of GLP-1 RAs, losing weight was associated with either a marked improvement or, in some cases, a deterioration, in mood; increase/decrease in anxiety/insomnia; and better control of a range of addictive behaviors. The challenges of accessing these medications were a hot topic as well. To the best of our knowledge, this is the first study documenting if and how GLP-1 RAs are perceived as affecting mood, mental health, and behaviors. Establishing a clear cause-and-effect link between metabolic diseases, depression and medications is difficult because of their possible reciprocal relationship, shared underlying mechanisms and individual differences. Further research is needed to better understand the safety profile of these molecules and their putative impact on behavioral and non-behavioral addictions.Peer reviewe

COVID-19 Pandemic Impact on Substance Misuse: A Social Media Listening, Mixed Method Analysis

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Submitted to Brain Sciences Manuscript ID: brainsci-1275549 on 10 June 2021. Reviewers’ comments received 23 June 2021. Revisions submitted 2 July 2021. Accepted 5 July 2021. Published online 9 July 2021. https://doi.org/10.3390/brainsci11070907 (registering DOI)The restrictive measures adopted during the COVID-19 pandemic modified some previously consolidated drug use patterns. A focus on social networks allowed drug users to discuss, share opinions and provide advice during a worldwide emergency context. In order to explore COVID-19-related implications on drug trends/behaviour and on most popular psychotropic substances debated, the focus here was on the constantly updated, very popular, Reddit social platform’s posts and comments. A quantitative and qualitative analysis of r/Drugs and related subreddits, using a social media listening netnographic approach, was carried out. The post/comments analysed covered the time-frame December 2019–May 2020. Between December 2019 and May 2020, the number of whole r/Drugs subreddit members increased from 619,563 to 676,581 members, respectively, thus increasing by 9.2% by the end of the data collection. Both the top-level r/Drugs subreddit and 92 related subreddits were quantitatively analysed, with posts/comments related to 12 drug categories. The drugs most frequently commented on included cannabinoids, psychedelics, opiates/opioids, alcohol, stimulants and prescribed medications. The qualitative analysis was carried out focussing on four subreddits, relating to some 1685 posts and 3263 comments. Four main themes of discussion (e.g., lockdown-associated immunity and drug intake issues; drug-related behaviour/after-quarantine plans’ issues; lockdown-related psychopathological issues; and peer-to-peer advice at the time of COVID-19) and four categories of Redditors (e.g., those continuing the use of drugs despite the pandemic; the “couch epidemiologists”; the conspirationists/pseudo-science influencers; and the recovery-focused users) were tentatively identified here. A mixed-methods, social network-based analysis provided a range of valuable information on Redditors’ drug use/behaviour during the first phase of the COVID-19 pandemic. Further studies should be carried out focusing on other social networks as well as later phases of the pandemic.Peer reviewe

Reflections on social media participation in young people and parents during the COVID-19 global pandemic: towards an understanding of the barriers and facilitators to use that support wellbeing

Aims: This study aimed to (1) explore perspectives of young people and parents on social media use during the COVID-19 pandemic, and (2) better understand how young people can use social media in ways that support wellbeing. Methods: Nine parents and eight young people participated in semi-structured interviews remotely via video in July 2020. Thematic analysis was performed on the data. Results: Four main themes were derived: (1) impact of the pandemic on social media use, (2) role of social media in young people’s lives, (3) challenges of social media, and (4) supporting young people to use social media in ways that promote wellbeing. Fourteen sub-themes were identified. Conclusions: Social media use increased within families during the pandemic, and was perceived as crucial for maintaining social connections. Benefits of social media were acknowledged. However, fear of missing out, exclusion, and comparisons were identified as challenges to wellbeing. School support was thought by young people to lack relevance. Barriers to parental support included lack of knowledge and experience, and young people favoured peer support. Findings implicate peer- based models to promote social media use that supports wellbeing, led by young people and in line with rights-based perspectives on digital technology use

From Fitspiration Posts to Food Shaming: Social Media’s Impact on Adolescent Girls’ Body Image

Social media is marketed as a way to connect and share with others all over the world—a fast way to connect to grandma down in Florida, do research from a university on the other side of the world, or even spark environmental change across the globe. So how, at the same time, can this be a harmful tool, one that has been identified as a cause of depression and even, in some cases, suicide in young girls? Social media sites like Instagram, FaceBook, and TikTok encourage users to post pictures and videos that show your “best self” alongside a rating system of visible comments and like counts on each post. While social comparison is not new, social media has amplified it allowing people to compare themselves to hundreds of people in with endless scrolling and the expectation to “be liked” by everyone. Research has shown social media comparison impacts individuals’ body image, particularly how it is affected by viewing other people’s bodies that fit societal expectations when theirs may not. Several studies note specifically how social media negatively impacts adolescent girls and their body image and self-worth through various trends and personalized critiques. Through the examination of fitspiration posts and food shaming hashtags, I detail examples of negative body image in the demographic of adolescent girls, and I call for awareness and changes in social media

Monitoring depressive symptoms using social media data

Social media data contains rich information about one's emotions and daily life experiences. In the recent decade, researchers have found links between people's behavior on social media platforms and their mental health status. However, little effort has been spent on mapping social media behaviors to the psychological processes underlying the psychopathological symptoms. Identifying these links may allow researchers to observe the trajectory of the illness through social media behaviors. The psychological processes examined in this thesis include affective patterns, distorted cognitive thinking and topics relevant to mental health status. In the first part of the thesis, we conducted two studies to explore methods to extract affective patterns from social media text. We demonstrated that mood fluctuations and mood transitions extracted from social media text reflect an individual’s depressive symptom level. In another study, we demonstrated that the affect from content not written by social media users themselves, such as quotes and lyrics, also reflects depressive symptoms, but the implications from these are different from content written by the users themselves. In the second part of the thesis, we identified distorted thinking from social media text. We found that these thinking patterns have a higher association with users' self-reported depressive symptom levels than affect extracted from users' text. In the last part of the thesis, we manually compiled topic dictionaries related to suicidal ideations according to the psychopathology literature. We found that users' suicidal risk levels can be estimated by using these topics. The estimation can be improved by combining these topics with results from a language model. The data-driven empirical studies in this thesis demonstrated that we can characterize the social media signals in a way that impacts our understanding of mental disorder symptoms. We blended data-driven methods such as machine learning, natural language processing and data science with theoretical insights from psychology

정신건강에서 사용자 내러티브와 자아성찰을 지원하는 대화형 에이전트 디자인

학위논문 (박사) -- 서울대학교 대학원 : 융합과학기술대학원 융합과학부(디지털정보융합전공), 2020. 8. 서봉원.In the advent of artificial intelligence (AI), we are surrounded by technological gadgets, devices and intelligent personal assistant (IPAs) that voluntarily take care of our home, work and social networks. They help us manage our life for the better, or at least that is what they are designed for. As a matter of fact, few are, however, designed to help us grapple with the thoughts and feelings that often construct our living. In other words, technologies hardly help us think. How can they be designed to help us reflect on ourselves for the better? In the simplest terms, self-reflection refers to thinking deeply about oneself. When we think deeply about ourselves, there can be both positive and negative consequences. On the one hand, reflecting on ourselves can lead to a better self-understanding, helping us achieve life goals. On the other hand, we may fall into brooding and depression. The sad news is that the two are usually intertwined. The problem, then, is the irony that reflecting on oneself by oneself is not easy. To tackle this problem, this work aims to design technology in the form of a conversational agent, or a chatbot, to encourage a positive self-reflection. Chatbots are natural language interfaces that interact with users in text. They work at the tip of our hands as if SMS or instant messaging, from flight reservation and online shopping to news service and healthcare. There are even chatbot therapists offering psychotherapy on mobile. That machines can now talk to us creates an opportunity for designing a natural interaction that used to be humans own. This work constructs a two-dimensional design space for translating self-reflection into a human-chatbot interaction, with user self-disclosure and chatbot guidance. Users confess their thoughts and feelings to the bot, and the bot is to guide them in the scaffolding process. Previous work has established an extensive line of research on the therapeutic effect of emotional disclosure. In HCI, reflection design has posited the need for guidance, e.g. scaffolding users thoughts, rather than assuming their ability to reflect in a constructive manner. The design space illustrates different reflection processes depending on the levels of user disclosure and bot guidance. Existing reflection technologies have most commonly provided minimal levels of disclosure and guidance, and healthcare technologies the opposite. It is the aim of this work to investigate the less explored space by designing chatbots called Bonobot and Diarybot. Bonobot differentiates itself from other bot interventions in that it only motivates the idea of change rather than direct engagement. Diarybot is designed in two chat versions, Basic and Responsive, which create novel interactions for reflecting on a difficult life experience by explaining it to and exploring it with a chatbot. These chatbots are set up for a user study with 30 participants, to investigate the user experiences of and responses to design strategies. Based on the findings, challenges and opportunities from designing for chatbot-guided reflection are explored. The findings of this study are as follows. First, participants preferred Bonobots questions that prompted the idea of change. Its responses were also appreciated, but only when they conveyed accurate empathy. Thus questions, coupled with empathetic responses, could serve as a catalyst for disclosure and even a possible change of behavior, a motivational boost. Yet the chatbot-led interaction led to surged user expectations for the bot. Participants demanded more than just the guidance, such as solutions and even superhuman intelligence. Potential tradeoff between user engagement and autonomy in designing human-AI partnership is discussed. Unlike Bonobot, Diarybot was designed with less guidance to encourage users own narrative making. In both Diarybot chats, the presence of a bot could make it easier for participants to share the most difficult life experiences, compared to a no-chatbot writing condition. Yet an increased interaction with the bot in Responsive chat could lead to a better user engagement. On the contrary, more emotional expressiveness and ease of writing were observed with little interaction in Basic chat. Coupled with qualitative findings that reveal user preference for varied interactions and tendency to adapt to bot patterns, predictability and transparency of designing chatbot interaction are discussed in terms of managing user expectations in human-AI interaction. In sum, the findings of this study shed light on designing human-AI interaction. Chatbots can be a potential means of supporting guided disclosure on lifes most difficult experiences. Yet the interaction between a machine algorithm and an innate human cognition bears interesting questions for the HCI community, especially in terms of user autonomy, interface predictability, and design transparency. Discussing the notion of algorithmic affordances in AI agents, this work proposes meaning-making as novel interaction design metaphor: In the symbolic interaction via language, AI nudges users, which inspires and engages users in their pursuit of making sense of lifes agony. Not only does this metaphor respect user autonomy but also it maintains the veiled workings of AI from users for continued engagement. This work makes the following contributions. First, it designed and implemented chatbots that can provide guidance to encourage user narratives in self-reflection. Next, it offers empirical evidence on chatbot-guided disclosure and discusses implications for tensions and challenges in design. Finally, this work proposes meaning-making as a novel design metaphor. It calls for the responsible design of intelligent interfaces for positive reflection in pursuit of psychological wellbeing, highlighting algorithmic affordances and interpretive process of human-AI interaction.최근 인공지능(Artificial Intelligence; AI) 기술은 우리 삶의 면면을 매우 빠르게 바꿔놓고 있다. 특히 애플의 시리(Siri)와 구글 어시스턴트 (Google Assistant) 등 자연어 인터페이스(natural language interfaces)의 확장은 곧 인공지능 에이전트와의 대화가 인터랙션의 주요 수단이 될 것임을 능히 짐작케 한다. 실상 인공지능 에이전트는 실생활에서 콘텐츠 추천과 온라인 쇼핑 등 다양한 서비스를 제공하고 있지만, 이들의 대부분은 과업-지향적이다. 즉 인공지능은 우리의 삶을 편리하게 하지만, 과연 편안하게 할 수 있는가? 본 연구는 편하지만 편하지 않은 현대인을 위한 기술의 역할을 고민하는 데에서 출발한다. 자아성찰(self-reflection), 즉 자신에 대해 깊이 생각해 보는 활동은 자기인식과 자기이해를 도모하고 배움과 목표의식을 고취하는 등 분야를 막론하고 널리 연구 및 적용되어 왔다. 하지만 자아성찰의 가장 큰 어려움은 스스로 건설적인 성찰을 도모하기 힘들다는 것이다. 특히, 부정적인 감정적 경험에 대한 자아성찰은 종종 우울감과 불안을 동반한다. 극복이 힘든 경우 상담 또는 치료를 찾을 수 있지만, 사회적 낙인과 잣대의 부담감으로 꺼려지는 경우가 다수이다. 성찰 디자인(Reflection Design)은 인간-컴퓨터상호작용(HCI)의 오랜 화두로, 그동안 효과적인 성찰을 도울 수 있는 디자인 전략들이 다수 연구되어 왔지만 대부분 다양한 사용자 데이터 수집 전략을 통해 과거 회상 및 해석을 돕는 데 그쳤다. 최근 소위 챗봇 상담사가 등장하여 심리상담과 치료 분야에 적용되고 있지만, 이 또한 성찰을 돕기보다는 효율적인 처치 도구에 머무르고 있을 뿐이다. 즉 기술은 치료 수단이거나 성찰의 대상이 되지만, 그 과정에 개입하는 경우는 제한적이라고 할 수 있다. 이에 본 연구는 성찰 동반자로서 대화형 에이전트인 챗봇을 디자인할 것을 제안한다. 이 챗봇의 역할은 사용자의 부정적인 감정적 경험 또는 트라우마에 대해 이야기할 수 있도록 도울 뿐 아니라, 그 과정에서 반추를 통제하여 건설적인 내러티브를 이끌어 내는 가이드를 제공하는 것이다. 이러한 챗봇을 설계하기 위해, 선행 연구를 기반으로 사용자의 자기노출(user self-disclosure)과 챗봇 가이드(guidance)를 두 축으로 한 디자인 공간(design space)을 정의하였다. 그리고 자기노출과 가이드의 정도에 따른 네 가지 자아성찰 경험을 분류하였다: 자기노출과 가이드가 최소화된 회상 공간, 자기노출이 위주이고 가이드가 최소화된 설명 공간, 자기노출과 챗봇이 이끄는 가이드가 혼합된 탐색 공간, 가이드를 적극 개입시켜 자기노출을 높이는 변화 공간이 그것이다. 본 연구의 목표는 상술된 디자인 공간에서의 성찰 경험과 과정을 돕는 챗봇을 구현하고, 사용자 실험을 통해 성찰 경험과 디자인 전략에 대한 반응을 수집 및 분석함으로써 챗봇 기반의 자아 성찰 인터랙션을 새롭게 제시하고 이에 대한 실증적 근거를 마련하는 것이다. 현재까지 많은 성찰 기술은 회상에 집중되어 있기에, 나머지 세 공간에서의 성찰을 지원하는 보노봇과 기본형반응형 일기봇을 디자인하였다. 또한, 사용자 평가를 바탕으로 도출한 연구결과를 통해 도래한 인간-인공지능 상호작용(human-AI interaction)의 맥락에서 성찰 동반자로서의 챗봇 기술이 갖는 의미와 역할을 탐구한다. 보노봇과 일기봇은 인간중심상담과 대화분석의 이론적 근거를 바탕으로 한 정서지능(emotional intelligence)과 절차지능(proecedural intelligence)을 핵심 축으로, 대화 흐름 제어(flow manager)와 발화 생성(response generator)을 핵심 모듈로 구현하였다. 먼저, 보노봇은 동기강화상담(motivational interviewing)을 기반으로 고민과 스트레스에 대한 내러티브를 이끌어내어, 이에 대한 해결을 위한 가이드 질문을 통해 변화를 위한 성찰을 돕는다. 챗봇의 구현을 위해, 동기강화상담의 네 단계 대화를 설정하고 각 단계를 구성할 수 있는 상담사 발화 행동을 관련문헌에서 수집 및 전처리 과정을 거쳐 스크립트화하였다. 또한, 사전 전처리된 문장이 맥락을 유지할 수 있는 대화에 쓰일 수 있도록, 대화의 주제는 대학원생의 어려움으로 한정하였다. 보노봇과의 대화가 사용자의 성찰에 미치는 영향과 이에 대한 인식을 탐색하기 위해 질적 연구방법을 사용하여 30명의 대학원생과 사용자 실험을 진행하였다. 실험결과, 사용자는 변화 대화를 유도할 수 있는 다양한 탐색 질문을 선호하였다. 또한, 사용자의 맥락에 정확히 들어맞는 질문과 피드백은 사용자를 더욱 적극적인 자기 노출로 이끌게 할 수 있음을 발견하였다. 그러나 챗봇이 마치 상담사처럼 대화를 이끌어갈 경우, 높아진 사용자의 기대 수준으로 인해 일부 사용자가 변화에 대한 동기를 표출하였음에도 불구하고 변화에 대한 자율성을 챗봇에 양도하려는 모습 또한 나타남을 분석하였다. 보노봇 연구를 바탕으로 일기봇은 챗봇 대신 사용자가 보다 적극적으로 성찰 내러티브를 전개할 수 있도록 디자인하였다. 일기봇은 트라우마에 대한 표현적 글쓰기를 지원하는 챗봇으로, 기본형 또는 반응형 대화를 제공한다. 기본형 대화는 트라우마에 대해 자유롭게 설명할 수 있는 대화 환경을 제공하고, 반응형 대화는 사용자가 작성한 내러티브에 대한 후속 인터랙션을 통해 과거의 경험을 재탐색하도록 하였다. 또한, 후속 인터랙션의 발화 행동은 다양한 상담치료에서 발췌하되 유저의 내러티브에서 추출한 감정어 및 인간관계 키워드를 활용하도록 하였다. 각 일기봇에 대한 반응을 비교 분석하기 위해, 챗봇 없이 도큐먼트에 표현적 글쓰기 활동만을 하는 대조군을 설정하고 30명의 사용자를 모집하여 각 조건에 랜덤으로 배정, 설문과 면담을 동반한 4일간의 글쓰기 실험을 진행하였다. 실험결과, 사용자는 일기봇과의 인터랙션을 통해 보이지 않는 가상의 청자를 상상함으로써 글쓰기를 대화 활동으로 인지하고 있음을 알 수 있었다. 특히, 반응형 대화의 후속 질문들은 사용자로 하여금 상황을 객관화하고 새로운 관점으로 생각해 볼 수 있는 효과를 거두었다. 반응형 대화에서 후속 인터랙션을 경험한 사용자는 일기봇의 인지된 즐거움과 사회성, 신뢰도와 재사용 의향에 대한 평가가 다른 두 조건에서보다 유의하게 높았다. 반면, 기본형 대화 참여자는 다른 두 조건에서보다 감정적 표현의 용이성과 글쓰기의 어려움을 각각 유의하게 높게, 그리고 낮게 평가하였다. 즉, 챗봇은 많은 인터랙션 없이도 청자의 역할을 수행할 수 있었지만, 후속 질문을 통한 인터랙션이 가능했던 반응형 대화는 더욱 적극적인 유저 참여(engagement)를 이끌어낼 수 있었다. 또한, 실험이 진행됨에 따라, 사용자가 반응형 일기봇의 알고리즘에 자신의 글쓰기 주제와 단어 선택 등을 맞게 바꾸어 가는 적응적(adaptive) 행동이 관찰되었다. 앞선 연구결과를 통해, 다양한 챗봇 디자인 전략을 바탕으로 사용자의 내러티브가 다르게 유도될 수 있으며, 따라서 서로 다른 유형의 성찰 경험을 이끌어낼 수 있음을 발견하였다. 또한, 자율적인 행위인 자아성찰이 기술과의 상호작용으로 호혜적 성질을 갖게 될 때 사용자의 자율성, 상호작용의 예측가능성과 디자인 투명성에서 발생할 수 있는 갈등관계(tensions)를 탐색하고 인공지능 에이전트의 알고리즘 어포던스(algorithmic affordances)를 논의하였다. 보이지 않는 챗봇 알고리즘에 의해 사용자의 성찰이 유도될 수 있다는 것은 기존의 인간-컴퓨터 상호작용에서 강조되는 사용자 제어와 디자인 투명성에서 전복을 초래하는 것처럼 보일 수 있으나, 상징적 상호작용(symbolic interaction)의 맥락에서 오히려 사용자가 알고리즘에 의해 지나간 과거에 대한 새로운 의미를 적극 탐색해나가는 과정이 될 수 있다. 본 연구는 이것을 새로운 디자인 메타포, 즉 의미-만들기(meaning-making)로 제안하고 알고리즘의 넛지(nudge)에 의한 사용자의 주관적 해석 경험(interpretive process)을 강조한다. 이것은 하나의 챗봇 알고리즘이라 할지라도 서로 다른 사용자의 다양한 성찰 경험을 유도해낼 수 있다는 것을 의미하며, 이러한 맥락에서 인공지능은 기존의 블랙 박스를 유지하면서도 사용자의 자율성을 보장할 수 있다. 본 연구는 우리와 협업하는 인공지능 챗봇 기술의 디자인에 대한 경험적 이해를 높이고, 이론을 기반으로 한 챗봇을 구현함으로써 디자인 전략에 대한 실증적 근거를 제시한다. 또한 자아 성찰 과정에 동행하는 동반자(companion)로서의 기술로 새로운 디자인 메타포를 제시함으로써 인간컴퓨터상호작용(HCI)의 이론적 확장에 기여하고, 사용자의 부정적 경험에 대한 의미 추구를 돕는 관계지향적 인공지능으로서 향후 현대인의 정신건강에 이바지할 수 있는 사회적, 산업적 의의를 갖는다.CHAPTER 1. INTRODUCTION １ 1.1. BACKGROUND AND MOTIVATION １ 1.2. RESEARCH GOAL AND QUESTIONS ５ 1.2.1. Research Goal ５ 1.2.2. Research Questions ５ 1.3. MAJOR CONTRIBUTIONS ８ 1.4. THESIS OVERVIEW ９ CHAPTER 2. LITERATURE REVIEW １１ 2.1. THE REFLECTING SELF １１ 2.1.1. Self-Reflection and Mental Wellbeing １１ 2.1.2. The Self in Reflective Practice １５ 2.1.3. Design Space ２２ 2.2. SELF-REFLECTION IN HCI ２６ 2.2.1. Reflection Design in HCI ２６ 2.2.2. HCI for Mental Wellbeing ３６ 2.2.3. Design Opportunities ４０ 2.3. CONVERSATIONAL AGENT DESIGN ４２ 2.3.1. Theoretical Background ４２ 2.3.2. Technical Background ４７ 2.3.3. Design Strategies ４９ 2.4. SUMMARY ６９ CHAPTER 3. DESIGNING CHATBOT FOR TRANSFORMATIVE REFLECTION ７１ 3.1. DESIGN GOAL AND DECISIONS ７１ 3.2. CHATBOT IMPLEMENTATION ７６ 3.2.1. Emotional Intelligence ７６ 3.2.2. Procedural Intelligence ７７ 3.3. EXPERIMENTAL USER STUDY ７９ 3.3.1. Participants ７９ 3.3.2. Task ８０ 3.3.3. Procedure ８０ 3.3.4. Ethics Approval ８０ 3.3.5. Surveys and Interview ８１ 3.4. RESULTS ８２ 3.4.1. Survey Findings ８２ 3.4.2. Qualitative Findings ８３ 3.5. IMPLICATIONS ８８ 3.5.1. Articulating Hopes and Fears ８９ 3.5.2. Designing for Guidance ９１ 3.5.3. Rethinking Autonomy ９２ 3.6. SUMMARY ９４ CHAPTER 4. DESIGNING CHATBOTS FOR EXPLAINING AND EXPLORING REFLECTIONS ９６ 4.1. DESIGN GOAL AND DECISIONS ９６ 4.1.1. Design Decisions for Basic Chat ９８ 4.1.2. Design Decisions for Responsive Chat ９８ 4.2. CHATBOT IMPLEMENTATION １０２ 4.2.1. Emotional Intelligence １０３ 4.2.2. Procedural Intelligence １０５ 4.3. EXPERIMENTAL USER STUDY １０６ 4.3.1. Participants １０６ 4.3.2. Task １０７ 4.3.3. Procedure １０７ 4.3.4. Safeguarding of Study Participants and Ethics Approval １０８ 4.3.5. Surveys and Interviews １０８ 4.4. RESULTS １１１ 4.4.1. Quantitative Findings １１１ 4.4.2. Qualitative Findings １１８ 4.5. IMPLICATIONS １２７ 4.5.1. Telling Stories to a Chatbot １２８ 4.5.2. Designing for Disclosure １３０ 4.5.3. Rethinking Predictability and Transparency １３２ 4.6. SUMMARY １３３ CHAPTER 5. DESIGNING CHATBOTS FOR SELF-REFLECTION: SUPPORTING GUIDED DISCLOSURE １３５ 5.1. DESIGNING FOR GUIDED DISCLOSURE １３９ 5.1.1. Chatbots as Virtual Confidante １３９ 5.1.2. Routine and Variety in Interaction １４１ 5.1.3. Reflection as Continued Experience １４４ 5.2. TENSIONS IN DESIGN １４５ 5.2.1. Adaptivity １４５ 5.2.2. Autonomy １４７ 5.2.3. Algorithmic Affordance １４８ 5.3. MEANING-MAKING AS DESIGN METAPHOR １５０ 5.3.1. Meaning in Reflection １５１ 5.3.2. Meaning-Making as Interaction １５３ 5.3.3. Making Meanings with AI １５５ CHAPTER 6. CONCLUSION １５８ 6.1. RESEARCH SUMMARY １５８ 6.2. LIMITATIONS AND FUTURE WORK １６１ 6.3. FINAL REMARKS １６３ BIBLIOGRAPHY １６５ ABSTRACT IN KOREAN １９２Docto