How should my chatbot interact? A survey on human-chatbot interaction design (original) (raw)
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International Journal of Human–Computer Interaction
The growing popularity of chatbots has brought new needs for HCI since it has changed the patterns of human interactions with computers. The conversational aspect of the interaction increases the necessity for chatbots to present social behaviors that are habitual in human-human conversations. In this survey, we argue that chatbots should be enriched with social characteristics that are coherent with users' expectations, ultimately avoiding frustration and dissatisfaction. We bring together the literature on disembodied, text-based chatbots to derive a conceptual model of social characteristics for chatbots. We analyzed 58 papers from various domains to understand how social characteristics can benefit the interactions and identify the challenges and strategies to designing them. Additionally, we discussed how characteristics may influence one another. Our results provide relevant opportunities to both researchers and designers to advance human-chatbot interactions.
Internet Science, 2019
Chatbots are emerging as interactive systems. However, we lack knowledge on how to classify chatbots and how such classification can be brought to bear in analysis of chatbot interaction design. In this workshop paper, we propose a typology of chatbots to support such classification and analysis. The typology dimensions address key characteristics that differentiate current chatbots: the duration of the user's relation with the chatbot (short-term and long-term), and the locus of control for user's interaction with the chatbot (userdriven and chatbot-driven). To explore the usefulness of the typology, we present four example chatbot purposes for which the typology may support analysis of high-level chatbot interaction design. Furthermore, we analyse a sample of 57 chatbots according to the typology dimensions. The relevance and application of the typology for developers and service providers are discussed.
An Overview of Chatbot Technology
IFIP Advances in Information and Communication Technology
The use of chatbots evolved rapidly in numerous fields in recent years, including Marketing, Supporting Systems, Education, Health Care, Cultural Heritage, and Entertainment. In this paper, we first present a historical overview of the evolution of the international community's interest in chatbots. Next, we discuss the motivations that drive the use of chatbots, and we clarify chatbots' usefulness in a variety of areas. Moreover, we highlight the impact of social stereotypes on chatbots design. After clarifying necessary technological concepts, we move on to a chatbot classification based on various criteria, such as the area of knowledge they refer to, the need they serve and others. Furthermore, we present the general architecture of modern chatbots while also mentioning the main platforms for their creation. Our engagement with the subject so far, reassures us of the prospects of chatbots and encourages us to study them in greater extent and depth.
My Chatbot Companion -a Study of Human-Chatbot Relationships
International Journal of Human-Computer Studies, 2021
There has been a recent surge of interest in social chatbots, and human-chatbot relationships (HCRs) are becoming more prevalent, but little knowledge exists on how HCRs develop and may impact the broader social context of the users. Guided by Social Penetration Theory, we interviewed 18 participants, all of whom had developed a friendship with a social chatbot named Replika, to understand the HCR development process. We find that at the outset, HCRs typically have a superficial character motivated by the users' curiosity. The evolving HCRs are characterised by substantial affective exploration and engagement as the users' trust and engagement in self-disclosure increase. As the relationship evolves to a stable state, the frequency of interactions may decrease, but the relationship can still be seen as having substantial affective and social value. The relationship with the social chatbot was found to be rewarding to its users, positively impacting the participants' perceived wellbeing. Key chatbot characteristics facilitating relationship development included the chatbot being seen as accepting, understanding and non-judgmental. The perceived impact on the users' broader social context was mixed, and a sense of stigma associated with HCRs was reported. We propose an initial model representing the HCR development identified in this study and suggest avenues for future research.
Future directions for chatbot research: an interdisciplinary research agenda
Computing
Chatbots are increasingly becoming important gateways to digital services and information—taken up within domains such as customer service, health, education, and work support. However, there is only limited knowledge concerning the impact of chatbots at the individual, group, and societal level. Furthermore, a number of challenges remain to be resolved before the potential of chatbots can be fully realized. In response, chatbots have emerged as a substantial research area in recent years. To help advance knowledge in this emerging research area, we propose a research agenda in the form of future directions and challenges to be addressed by chatbot research. This proposal consolidates years of discussions at the CONVERSATIONS workshop series on chatbot research. Following a deliberative research analysis process among the workshop participants, we explore future directions within six topics of interest: (a) users and implications, (b) user experience and design, (c) frameworks and p...
JMIR Human Factors
Background Although social chatbot usage is expected to increase as language models and artificial intelligence improve, very little is known about the dynamics of human-social chatbot interactions. Specifically, there is a paucity of research examining why human-social chatbot interactions are initiated and the topics that are discussed. Objective We sought to identify the motivating factors behind initiating contact with Replika, a popular social chatbot, and the topics discussed in these interactions. Methods A sample of Replika users completed a survey that included open-ended questions pertaining to the reasons why they initiated contact with Replika and the topics they typically discuss. Thematic analyses were then used to extract themes and subthemes regarding the motivational factors behind Replika use and the types of discussions that take place in conversations with Replika. Results Users initiated contact with Replika out of interest, in search of social support, and to c...
Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems, 2018
Chatbots are emerging as an increasingly important area for the HCI community, as they provide a novel means for users to interact with service providers. Due to their conversational character, chatbots are potentially effective tools for engaging with customers, and are often developed with commercial interests at the core. However, chatbots also represent opportunities for positive social impact. Chatbots can make needed services more accessible, available, and affordable. They can strengthen users' autonomy, competence, and (possibly counter-intuitively) social relatedness. In this SIG we address the possible social benefits of chatbots and conversational user interfaces. We will bring together the existing, but disparate, community of researchers and practitioners within the CHI community and broader fields who have an interest in chatbots. We aim to discuss the potential for chatbots to move beyond their assumed role as channels for commercial service providers, explore how...
Chatbots – changing user needs and motivations.
Interactions, 2018
Chatbots have been around for decades. However, the real buzz around this technology did not start until the spring of 2016. Reasons for the sudden renewed interest in chatbots include massive advances in artificial intelligence (AI) and a major usage shift from online social networks to mobile-messaging applications such as Facebook Messenger, Telegram, Slack, Kik, and Viber. The first of these reasons holds promise that intelligent chatbots may well be within reach. The second concerns service providers' need to reach users in the context of mobile messaging. However, in spite of these drivers, current chatbot applications suggest that conversational user interfaces still face substantial challenges, generally speaking, as well as for the field of human-computer interaction (HCI). Chatbots imply not only a change in the interface between users and technology; they also imply changing user dynamics and patterns of use.
Chatbots Explain Themselves: Designers' Strategies for Conveying Chatbot Features to Users
Journal on Interactive Systems, 2018
Recently, text-based chatbots had a rise in popularity, possibly due to new APIs for online social networks and messenger services, and development platforms that help dealing with all the necessary Natural Language Processing. But, as chatbots use natural language as interface, their users may struggle to discover which sentences the chatbots will understand and what they can do. Because of that it is important to support their designers in deciding how to convey the chatbots’ features, as this might determine whether the user will continue chatting or not. In this work, our goal is to analyze the communicative strategies used by popular chatbots when conveying their features to users. We used the Semiotic Inspection Method (SIM) for that end, and as a result we were able to identify a series of strategies used by the analyzed chatbots for conveying their features to users. We then consolidate these findings by analyzing other chatbots. Finally, we discuss the use of these strategi...
Proceedings of the 2nd Conference on Conversational User Interfaces
Conversational agents have transcended into multiple industries with increased ability for user engagement in intelligent conversation. Conversations with chatbots are different from interpersonal communication in terms of turntaking, intentions, and behavior. We study de-identified chat logs across 30 conversations with a well-recognized chatbot to understand (i) how people create turns in conversation to perform 'social action', extending human experiences and knowledge (ii) how people express typical human constructs like emotion in their interaction with chatbots, and, (iii) what are the discursive strategies used by people to create 'shared meaning' and identity for themselves. Our findings reveal conversational expectations and behavior of users being similar to those in human-to-human sharing (how people talk), but greater diversity in the nature of information shared (what they talk about). This can advance discussion both in how we can design conversational agents to be more intelligible, as well as sensitive to unnecessary information disclosure.