Artificially intelligent (AI) agents increasingly occupy roles once served by humans in computer-mediated communication (CMC). Technological affordances like emoji give interactants (humans or bots) the ability to partially overcome the limited nonverbal information in CMC. However, despite the growth of chatbots as conversational partners, few CMC and human-machine communication (HMC) studies have explored how bots' use of emoji impact perceptions of communicator quality. This study examined the relationship between emoji use and observers' impressions of interpersonal attractiveness, CMC competence, and source credibility; and whether impressions formed of human versus chatbot message sources were different. Results demonstrated that participants rated emoji-using chatbot message sources similarly to human message sources, and both humans and bots are significantly more socially attractive, CMC competent, and credible when compared to verbal-only message senders. Results are discussed with respect to the CASA paradigm and the human-to-human interaction script framework.

a b s t r a c tTwitter's design allows the implementation of automated programs that can submit tweets, interact with others, and generate content based on algorithms. Scholars and end-users alike refer to these programs to as "Twitterbots." This two-part study explores the differences in perceptions of communication quality between a human agent and a Twitterbot in the areas of cognitive elaboration, information seeking, and learning outcomes. In accordance with the Computers Are Social Actors (CASA) framework (Reeves & Nass, 1996), results suggest that participants learned the same from either a Twitterbot or a human agent. Results are discussed in light of CASA, as well as implications and directions for future studies.

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In this manuscript we discuss the increasing use of machine agents as potential sources of support for humans. Continued examination of the use of machine agents, particularly chatbots (or “bots”) for support is crucial as more supportive interactions occur with these technologies. Building off extant research on supportive communication, this manuscript reviews research that has implications for bots as support providers. At the culmination of the literature review, several propositions regarding how factors of technological efficacy, problem severity, perceived stigma, and humanness affect the process of support are proposed. By reviewing relevant studies, we integrate research on human-machine and supportive communication to organize, extend, and provide a foundation for the growing body of work on machine agents for support.

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