Chi-Lan Yang

Laugh tracks are a well-known technique for amplifying humor perception through audio. However, it remains unclear how such amplification transfers to VR, where audiences are embodied as virtual co-viewers in emerging shared entertainment spaces. To address this, we examined two design dimensions of virtual audience laughter: its audiovisual modality (the sensory channels through which laughter is expressed) and its temporal synchrony (the co-occurrence of audience and viewer laughter). In Study 1 (N = 18), we found that the audiovisual modality influenced social presence. It increased social presence when visual and auditory cues were aligned and had an inverse effect when they were not aligned. Study 2 (N = 24) revealed that temporal synchrony evoked perceived bidirectional exchange when audience reactions were synchronized with co-viewers’ laughter, even though the audience did not actually communicate with the viewer. Across both studies, participants viewing alone reported a sense of shared laughter with co-viewers and feeling they were not laughing alone, although neither dimension amplified humor perception. We propose the Embodied Laugh Track, a virtual audience design that shapes the social context of viewing, opening a new design axis for shared virtual entertainment beyond comedy.

Opinionated chatbots are increasingly present on online platforms and have the potential to shape public discourse by influencing individuals’ viewpoints before they engage in discussions. Despite their growing presence, the impact of interacting with opinionated chatbots on subsequent online interactions remains largely unexplored. This study investigated how exposure to different types of opinionated chatbots, specifically those expressing opposing, reinforcing, or balanced viewpoints, affected participants’ subsequent online discussions. In a controlled experiment with 83 participants, we found that interacting with an opinionated chatbot that consistently opposed participants’ arguments led to greater shifts in opinion, indicating enhanced openness to revising one’s initial stance. Conversely, participants who interacted with a chatbot that consistently reinforced their views were more likely to adopt more agreeable communication styles in subsequent conversations with others. Furthermore, interactions with different types of opinionated chatbots resulted in varying levels of trust, as well as different perceptions of chatbots and human interlocutors. Our findings indicate that opinionated chatbots can influence both individuals’ opinions on social topics and their communication behaviors in online environments. This presents a trade-off for future designers seeking to facilitate cognitive flexibility in changing opinions while maintaining positive user experiences and trust in the chatbots during public discourse. We discuss the implications for designing opinionated chatbots to promote more constructive and less polarized online discussions.

Despite the importance of social support in mental health, people often lack access to it. Chatbots have emerged as an alternative approach to providing social support, yet people demonstrate reluctance to embrace this approach. Drawing on social identity theory, we examine how shared similarities between people and chatbots—particularly in attitude, personality, and experience —influence people’s willingness to seek social support from chatbots. We conducted three experiments (N = 205), each focusing on one dimension of similarity. Our results showed that participants were more willing to seek social support from chatbots with similar attitudes and experiences and that displayed extroversion. Furthermore, similarity in attitude boosted participants’ willingness to seek support from chatbots by enhancing their closeness with chatbots, and similarity in experience increased it by improving both closeness and perceived chatbots’ ability to offer support. Implications for designing chatbots for social support are discussed.

Conversational agents are increasingly used in health contexts to provide both informational and relational support. While prior research has shown that persona cues influence user perceptions, evaluative criteria such as competence and warmth have typically been treated as fixed dimensions, without examining how they shift depending on users’ role interpretations. We conducted a qualitative study in which 45 participants interacted with health chatbots framed as expert, coach, partner, or influencer for two weeks, followed by semi-structured interviews analyzed using thematic analysis. Findings show that participants actively categorized chatbot roles and shaped their interaction patterns accordingly. When responses diverged from expectations, participants reinterpreted the role to maintain role–behavior alignment. Competence and warmth functioned as role-dependent evaluative criteria: competence dominated in authority-oriented roles, whereas warmth authenticity became critical in relational roles, where perceived inauthenticity actively undermined trust. These findings suggest that persona design should align role cues with role-specific evaluative expectations.