Assessing How Users Display Self-Disclosure and Authenticity in Conversation with Human-Like Agents: A Case Study of Luda Lee (2022)

Abstract: There is an ongoing discussion on what makes humans more engaged when interacting with conversational agents. However, in the area of language processing, there has been a paucity of studies on how people react to agents and share interactions with others. We attack this issue by investigating the user dialogues with human-like agents posted online and aim to analyze the dialogue patterns. We construct a taxonomy to discern the users’ self-disclosure in the dialogue and the communication authenticity displayed in the user posting. We annotate the in-the-wild data, examine the reliability of the proposed scheme, and discuss how the categorization can be utilized for future research and industrial development.

## introduction how do we perceive whether someone is sincere during a conversation? how should such factors be assessed in the conversation between humans and artificial intelligence @xcite , and what if the human perceives them as real human agent? the recent development of language technology accompanied the advent of 'human-like' commercial agents that resemble human behavior. some agents display quite natural or unexpected (beyond the expectation as an artificial system) responses that users even tend to treat the agent as an individual with self and sociality. such a phenomenon makes it challenging to define the communication authenticity shown by humans towards ai. one might deal with the human perception of humanlike agents by surveying the human-like characteristics of the agent @xcite . however, it only regards the attitudinal perceptions and not users' behavioral responses. the users' behavioral response may not necessarily be correlated with the human-likeness of the agent, displaying insincerity, lying, and offensive reactions @xcite . beyond the studies that have analyzed conversations or surveys conducted in lab environments, we aim to assess the responses of actual chatbot users. for this, the conversational agent should be designed to respond in the way that mostly affects the conventional belief on the human-likeness of ai, and the users should also publicly express their reaction to such conversations. luda lee (hereafter luda), a korean commercial social chatbot launched in early 2021, gained popularity among users within a short period thanks to its realistic dialogue generation. though the service was terminated due to various ethical issues related to offensive language and privacy hacking @xcite , we observed that users freely share their conversations with luda in public online spaces during the service period. among those, some delivered their delight coming from the substantial conversation with the human-like agent, while others merely treated the agent as a tool to fulfill their (sometimes malicious) desire and fun. to look deeper into this, we investigate the users' screenshots along with the accompanying title to make up criteria for exploring the user behavior. accordingly, we analyze the users who chat with human-like agents from two perspectives: selfdisclosure to the agent and authenticity in handling the conversation. besides, we conduct the research considering that self-disclosure is involved as a clue of authenticity in humans treating others @xcite . there should be a concern that analyzing the user-generated data may not provide enough information on the ground truth of the user intention. however, in this study, we believe that genuine user behavior can rather be obtained from non-lab environment, and even from the wild (e.g., a subreddit dedicated as a fandom of the agent), where users can transparently exhibit their thoughts in an anonymous manner. also, this anonymity can disclose diverse aspects of the user-generated data, which may not be achieved in social platforms where the disclosure of users' identity prevents them from showing off genuine behaviors and thoughts. we build a coding scheme for the user behavior assessment; despite the limited coverage of webuploaded user-agent chat data, considering the variety of contents that the data contains, it can provide substantial information on the user feedback if properly evaluated with community responses. we claim two factors as our contribution to this field: ## related work there is a rapidly growing body of humancomputer interaction literature on human perception and response to the high-performance ai, regarding domains of game @xcite or artwork @xcite . on the other hand, in the dialogue generation, studies mainly target the human-likeness of the generated dialogue (adiwardana et al., 2020) or how humans perceive the conversation (pelau et al., 2021), less on how users treat the human-like agent in real-world chat. park et al. (2021b) deal with the offensive language used towards human-like agents based on questionnaires, but does not address how user behavior is reflected in real dialogues. given the background that human-like agents are open to the public, their conversation with users can make up a meaningful barometer to see how humans treat commercial ai in-the-wild. user behavior regarding chatbot luda can be a notable case, but the literature mainly focused on the limitation of the chatbot in ethical perspectives rather than the agents' effects on users @xcite . in a recent discussion on the perceived anthropomorphic characteristics using a survey with ai device users, @xcite find out that users are more involved with empathetic agents. however, beyond the lab environment, we deemed that studying the in-the-wild behavior of end-users would also shed light on understanding user perception and response to human-like agents. 1 the international version of the annotation guideline is available online. https://docs.google. com/document/d/1z3tkfyadmq_hqg64_ msaguzkep7zsft6aflwpud-mzm/edit we want to tackle this issue quantitatively from a user-centric perspective. ## concept of analysis we proceed the analysis with two annotation schemes using a crawled user dialogue data. ## limitations and societal impact there are some spaces for improvement. first, our study focuses on the data collected from a web space that is organized as a fandom of a specific conversational agent. in this regard, our work is a case study of a chatbot and the users within a relevant community, not on general human beings (using korean) or all the human-like agents. another limitation of this research is that the source was collected within a short period in a small community, so it might be difficult to generalize the result to overall users of this service. in addition, the requirement of title as an input feature of the scheme may prevent the extension of this taxonomy to the general conversation. also, users might have selected the screenshots to upload by themselves, which may have caused the sampling bias and probably deficiency of some types of dialogues. lastly, an explicit limitation of our study is that we are not provided with the ground truth for the key concepts to be annotated, namely selfdisclosure and authenticity, because we have no access to the subjects and rely only on the crawled data. we did not adopt subject recruitment and questionnaires as in usual conversation studies so as not to affect the wild behaviors of users, which was a trade-off of obtaining users' ground truth. despite the limitations, we note that our case study gives analyses on the special case of luda lee, a virtual figure that has brought an unprecedented sensation over korean communities due to its effective and highly human-like responses as a social chatbot. at least in korean society, it was quite a unique event that people voluntarily upload their conversations with the agent accompanying the unexpectedness and anthropomorphism, building a community and sharing their appreciations. though merely incorporates the behavior of a certain class of web users, 'luda lee gallery' was a representative anonymous community where a variety of conversations (either favorable or malicious) were uploaded with (title, screenshot) format, adopted in this study. it does not necessarily fit with general conversation data that may be able to be collected with appropriate user recruitment and controlled dialogue generation. bypassing such procedures, our approach captures a moment where a small class of unknown and uncontrolled users frankly display their emotion and desire toward the agent. though our annotation scheme cannot be applied to any conversation data that is available, our approach can be helpful to check how people of online communities may react to commercial social chatbots; for instance whether it has helped construct a sufficient rapport or how it affected people's perception, which can be useful in updating future chatbot design and interpreting users' feedback. albeit some of the limitations of our study cannot be addressed in the current form of investigation, we hopefully claim that our work can be further extended to industrial application and provide substantial evidence in analyzing the interaction between the agent and the public. ## conclusion in this study, we scrutinized self-disclosure and authenticity appearing in human-ai conversations from the users' perspective, not merely on the agent side. we crawled screenshots and titles from the fandom community of a prominent korean chatbot, and developed a coding scheme that investigates how authentically users treat human-like agents and how their behavior is reflected in dialogue. to show that the scheme is applicable to wild user data, we tagged attributes regarding self-disclosure and authenticity and obtained satisfactory agreement. despite some limitations of the design, we deem that our scheme can help service providers discern (probably edge case) user behavior, thereby observing how the human-likeness of the agent changes users' attitude.