Chatbots are increasingly used to provide social support for individuals with mental health challenges. However, a systematic analysis of the types and directionality of support within chatbot use remains lacking. This study establishes a framework for understanding reciprocal social support exchanges in human-chatbot relationships, focusing on the popular chatbot, Replika. By analyzing 496 posts and 20,494 comments from the largest Replika community on Reddit, we identified 27 support subcategories, organized into five main types (functional, informational, emotional, esteem, and network) and two directions (chatbot-receiving and chatbot-giving). Our findings reveal significant yet controversial issues, such as subscription services and chatbot-displayed affection. Notably, "user teaching chatbot"emerged as a core aspect of the human-chatbot relationship, covering how users actively guide and refine the chatbot's learning or algorithm. This study constructs a novel social support framework for chatbot use, highlighting the potential for reciprocal support exchanges between users and chatbots.

As artificial intelligence (AI) judges are increasingly pervasive in decision-making, it is important to investigate candidates’ reactions to decisions made by human–AI hybrid juries. This study investigates candidates’ attribution of credit for success and blame for failure to the three agents in question: a human judge, an algorithmic judge, and the candidate oneself. An experiment with 3 (jury type: human-dominated, algorithm-dominated, vs. equally dominated) × 2 (decision outcome: positive vs. negative) between-subjects factorial design was conducted, with 346 valid responses. Our findings demonstrate a partial ingroup-serving attribution dependent on the outcome favorability and a significant effect of relative power status within the human–AI hybrid jury on grouping and attribution. This study reflects the fluidity of identity and self-categorization of human users when facing AI and other humans. We propose that people take a utility-oriented glance at AI in multi-agent decision-making situations.

When someone intimately discloses themselves to a robot, does that make them like the robot more? Does a robot’s reciprocal disclosure contribute to a human’s liking of the robot? To explore whether these disclosure-liking effects in human–human interaction also apply to human–robot interaction, we conducted a between-subjects lab experiment to examine how self-disclosure intimacy (intimate vs. non-intimate) and reciprocal self-disclosure (yes vs. no) from the robot influence participants’ social perceptions (i.e., likability, trustworthiness, and social attraction) toward the robot. None of the disclosure-liking effects were confirmed by the results. In contrast, reciprocal self-disclosure from the robot increased liking in intimate self-disclosure but decreased liking in non-intimate self-disclosure, indicating a crossover interaction effect on likability. A post-hoc analysis was conducted to further understand these patterns. Implications in terms of the computers are social actors (CASA) paradigm were discussed.

Present-day power users of AI-powered social chatbots encounter various uncertainties and concerns when forming relationships with these virtual agents. To provide a systematic analysis of users’ concerns and to complement the current West-dominated approach to chatbot studies, we conducted a thorough observation of the experienced uncertainties users reported in a Chinese online community on social chatbots. The results revealed four typical uncertainties: technical uncertainty, relational uncertainty, ontological uncertainty, and sexual uncertainty. We further conducted visibility and sentiment analysis to capture users’ response patterns toward various uncertainties. We discovered that users’ identification of social chatbots is dynamic and contextual. Our study contributes to expanding, summarizing, and elucidating users’ experienced uncertainties and concerns as they form intimate relationships with AI agents.