The growing popularity of fitness influencers on Instagram has created a competitive space where human and AI-generated influencers vie for user engagement. This study examines how agent type (human vs. AI), gender, and age influence perceptions of interpersonal attraction, credibility, and social presence. Using a 2 × 2 × 2 experimental design, 161 participants rated mock Instagram posts from influencers across these categories. Results showed that human influencers were rated significantly higher in social attraction, credibility, and social presence than AI influencers. Younger influencers were perceived as more physically attractive, while older influencers scored higher in character and social presence. Regardless of agent type, female influencers were rated higher in physical attraction. These findings highlight persistent demographic biases and a strong preference for human connection in digital fitness spaces, offering key insights for brands, marketers, and influencer strategy.

Abstract Voice-based assistants (VBAs) are transforming how we interact with technology. We rely heavily on VBAs (such as Siri, Alexa and Google Assistant) to manage our daily routines and tasks. The current study extends past research on blurting to the human–machine communication (HMC) context. An online survey (n = 185) was conducted to investigate blurting with a VBA. We analysed open-ended accounts of instances in which participants had blurted during conversations with a VBA. In addition, findings show that blurting with a VBA is related to the less sophisticated views of interpersonal arguments and higher argument seeking, verbal aggressiveness, psychological reactance, extraversion and neuroticism. Our findings suggest that although people blurt with VBAs, in line with the general Computers Are Social Actors paradigm, the reasons are unique to HMC.

The purpose of this experiment was to examine the impact of computer-mediated word-of-mouth communication (WOM) on perceptions of a social robot instructing students. We hypothesized that participants receiving positive WOM about the social robot instructor would rate the robot higher for perceived credibility, attraction (social and task), social presence, and report higher levels of affective learning and state motivation to learn versus those participants receiving negative WOM (or no WOM) about the same performance. Results demonstrated that there are some factors in evaluating robot performance that are beyond the actual abilities of the robot. Results are examined using the Heuristic-Systematic Processing model, and the impact of WOM on perceptions of social robots in the environment. As a result of these findings, designers and practitioners need to be conscious of WOM.

This experiment was conducted to determine interpersonal impressions of two types of compliance-gaining strategies (warning and obligation) used to ask for the consideration of robot rights. Findings demonstrated that the strategy of obligation produced higher levels of petitioner perceived caring (a factor of source credibility) and higher perceptions of task attraction. Social attraction, competence, and character did not significantly differ by type of message strategy utilized.

Although both HRI and Communication Science often trace their origins to the transdisciplinary cybernetics of the 20th century, they have since developed in relative isolation, with scant scholarly exchange concerning the similarities and differences in their assumptions, insights, and approaches. The purpose of this half-day workshop is to explore the ways in which traditional communication theory and Human-Machine Communication theory from the discipline of Communication Science can be applied and be utilized in HRI studies.

The current study examines the pratfall effect and interpersonal impressions of how humans perceive robots that appear forgetful and apologetic regarding information given to them by a human. Results demonstrated that the non-forgetful robot was perceived higher for interpersonal impressions when compared to the forgetful and forgetful apologizing robots.

The purpose of this study was to test the strength of the machine heuristic; specifically when suspicion is primed in a message receiver concerning the veracity of information from a robot delivering news. Results demonstrate that low suspicion led to higher credibility evaluations, which consequently increased behavioral intentions. Findings are discussed in light of the MAIN model.

Extending previous research on the human-to-human interaction script, the MAIN model, and the Computers Are Social Actors (CASA) paradigm, this study examines the influence of weather-related communication sources with varying levels of expertise and agency cues (i.e. human versus bot) on impressions of communication quality. Specifically, an experimental design was used to measure people’s impressions of source credibility, task and social attraction, computer-mediated communication competence, and intent to interact in the future for three information sources: a professional meteorologist, an amateur meteorologist, and a weather Twitterbot. Results demonstrated similar perceptions of communication quality for the three Twitter agents. However, the Twitterbot was rated as significantly less socially attractive than the professional meteorologist and as significantly more task attractive than the amateur meteorologist delivering the same messages. Agency-related cues appear to trigger heuristics that account for these differences in perception. Implications for the use of Twitterbots in weather-related and information-sharing contexts are discussed.

This panel will lay the foundation for understanding the needs of Large Systems so that OO practitioners can:• appreciate the problems faced;• understand the issues involved; and• re-orient the approaches to provide a viable solution when participating in similar efforts.Specifically, this panel will establish the foundation for discussions on Large Systems by establishing concepts, exposing terminology, and highlighting the state-of-the art.Large applications are usually complex and display one or more of the following dimensions of largeness:• Processing power: requiring tens to hundreds of gigabytes of memory and hundreds of gigaflops performance• High connectivity: highly-connected, systems can show aggregate behavior with complex characteristics: they can become chaotic.• Online access: Archival of terabytes of information, with the need to provide online access to information• Archival and online retrieval: The two technologies (database and archival storage), however, currently do not interoperate. There is a need to develop interfaces to integrate these two technologies.• Data-intensive scientific applications: These involve constructing a data handling infrastructure that simplifies the effort required to maintain petabyte archives, identify relevant data sets within the archive, move, the data to processing platforms, and distribute the data sets across multiple nodes.• Internet access: Large applications often require simultaneous access of information by millions of users worldwide;they must provide acceptable response times.• Scaleable architecture: The systems must be prepared to support exponential growth of application load.Is OOT up to the task? OO practitioners will be encountering some of these application domains in the near future. Some may have already gained some experience in trying to solve these problems. However, the literature in OO does not provide sufficient evidence to believe that OO is ready for such large systems today. Part of the problem is the cross-disciplinary nature of these problems requires a steep learning curve for OO practitioners to be effective. The modeling of these problems with an OO approach is also a challenge. Current 00 methods do not do a good job of supporting multiple views of a domain, and multiple layers of a complex application domain.