Chatbots are often designed to mimic social roles attributed to humans. However, little is known about the impact of using language that fails to conform to the associated social role. Our research draws on sociolinguistic to investigate how a chatbot’s language choices can adhere to the expected social role the agent performs within a context. We seek to understand whether chatbots design should account for linguistic register. This research analyzes how register differences play a role in shaping the user’s perception of the human-chatbot interaction. We produced parallel corpora of conversations in the tourism domain with similar content and varying register characteristics and evaluated users’ preferences of chatbot’s linguistic choices in terms of appropriateness, credibility, and user experience. Our results show that register characteristics are strong predictors of user’s preferences, which points to the needs of designing chatbots with register-appropriate language to improve acceptance and users’ perceptions of chatbot interactions.

Chatbots’ growing popularity has brought new challenges to HCI, having changed the patterns of human interactions with computers. The increasing need to approximate conversational interaction styles raises expectations for chatbots to present social behaviors that are habitual in human–human communication. In this survey, we argue that chatbots should be enriched with social characteristics that cohere 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 56 papers from various domains to understand how social characteristics can benefit human–chatbot 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.

Designing chatbots that produce language that is natural and appropriate to a given context is critical in satisfying user expectations. Currently, little is known about how a chatbot's linguistic choices should be designed to conform with the language humans produce in similar contexts. In this paper, we draw on existing sociolinguistic theory to adapt a technique calledregister analysis to (a) characterize the linguistic register used by humans in a specific conversational context; and (b) drive chatbot language design. Our exploratory study investigates the application of register analysis for tourist assistants chatbots and shows how the results could be used to develop them to adopt the appropriate register.

Chatbots focusing on a narrow domain of expertise are in great rise. As several tasks require multiple expertise, a designer may integrate multiple chatbots in the background or include them as interlocutors in a conversation. We investigated both scenarios by means of a Wizard of Oz experiment, in which participants talked to chatbots about visiting a destination. We analyzed the conversation content, users' speech, and reported impressions. We found no significant difference between single- and multi-chatbots scenarios. However, even with equivalent conversation structures, users reported more confusion in multi-chatbots interactions and adopted strategies to organize turn-taking. Our findings indicate that implementing a meta-chatbot may not be necessary, since similar conversation structures occur when interacting to multiple chatbots, but different interactional aspects must be considered for each scenario.

Leveraging the pull request model of social coding platforms, Open Source Software (OSS) integrators review developers' contributions, checking aspects like license, code quality, and testability. Some projects use bots to automate predefined, sometimes repetitive tasks, thereby assisting integrators' and contributors' work. Our research investigates the usage and impact of such bots. We sampled 351 popular projects from GitHub and found that 93 (26%) use bots. We classified the bots, collected metrics from before and after bot adoption, and surveyed 228 developers and integrators. Our results indicate that bots perform numerous tasks. Although integrators reported that bots are useful for maintenance tasks, we did not find a consistent, statistically significant difference between before and after bot adoption across the analyzed projects in terms of number of comments, commits, changed files, and time to close pull requests. Our survey respondents deem the current bots as not smart enough and provided insights into the bots' relevance for specific tasks, challenges, and potential new features. We discuss some of the raised suggestions and challenges in light of the literature in order to help GitHub bot designers reuse and test ideas and technologies already investigated in other contexts.