Large language models (LLMs) are increasingly being used as ad hoc therapists. While prior research has found that LLMs outperform human counselors in generating single-turn empathetic responses, fewer studies have compared their behaviors across multi-turn sessions. In this study, we compare the session-level behaviors of human peer counselors with those of an LLM, both trained on the same manual to deliver multi-turn, single-session Cognitive Behavioral Therapy (CBT). Our three-phase, mixed-methods study involved: (a) an 18-month ethnography of a peer support platform, where seven counselors iteratively refined CBT prompts through 110 self-counseling sessions and 60 weekly focus groups; (b) a novel session generation method that allows direct, controlled comparison of human and LLM counselors under matched conditions—client responses were drawn from publicly available human-led CBT sessions while counselor responses were generated by a CBT-prompted LLM; and (c) expert evaluations conducted by three licensed clinical psychologists. Through data triangulation, our results show a trade-off. Human peer counselors use relational techniques to interpret subtle cues, adapt CBT to users’ values and cultural contexts, and use strategies such as small talk and contextually relevant self-disclosure to build rapport and guide the session, but often at the expense of session structure and therapeutic focus. LLM counselors, on the other hand, demonstrate greater methodological adherence to CBT techniques, but struggle to sustain turn-taking, frequently fail to distinguish between clinically important and trivial content, and are more prone to lecturing and imposing solutions. LLM counselors also tend to produce “deceptive empathy”, excessively anthropomorphic responses that can inflate user expectations of genuine human care. Taken together, our findings imply that while LLMs may outperform human counselors when generating a single-turn interaction, their ability to lead multi-turn sessions is more limited, highlighting that therapy cannot be reduced to a standalone natural language processing (NLP) task. We conclude by mapping concrete design opportunities and ethical guardrails for hybrid human-AI systems, emphasizing the risks of over-attributing human relational subjectivity to current LLMs.
Reaction videos (RVs) are surging in popularity, emerging as a distinctive facet of participatory culture on modern social video-sharing platforms such as YouTube, TikTok, and Twitch. This study aims to explore not only the motivations and engagement patterns of viewers towards RVs, but also the underlying nature of the virality and community-building phenomena that this sub-genre of video content fosters. We conducted 16 semi-structured interviews with individuals who identified as regular viewers of reaction videos to gain a deeper understanding of how they discover reaction videos (RQ1), the values that drive their motivations for viewing RVs (RQ2), and the ways viewers engage with RVs (RQ3). Our research highlights the variety of original content that RVs utilize, ranging from movie trailers to music releases, and the different engagement strategies viewers rely on in their consumption of RVs. Our research highlights the wide range of original content that RVs engage with, from movie trailers to music releases, as well as the diverse engagement strategies viewers employ when consuming RVs. Our findings highlight the significance of emotional connections with reactors, the value of communal experiences centered on shared interests, and the role of RVs in facilitating content discovery and critique. Drawing on viewers’ experiences with RVs, this work offers a behind-the-scenes perspective on the complex dynamics of spectatorship and its influence on the production and consumption of content in today’s digital landscape. We further contribute to a nuanced and comprehensive understanding of reaction video culture.
As more people meet, interact, and socialize online, Social Virtual Reality (VR) emerges as a technology that bridges the gap between traditional face-to-face and online communication. Unlike traditional screen-based applications, Social VR provides immersive, spatial, and three-dimensional social interactions, making it a potential tool for enhancing remote collaborations. Despite the growing interest in Social VR, research on its role in collaboration remains fragmented, calling for a synthesis to identify research gaps and future directions. We conducted a 20-year scoping review, screening 2,035 articles and identifying 62 articles that addressed how Social VR has supported collaboration. Our analysis shows three key levels of support: Social VR can enhance individual perceptions and experiences within their groups, foster team dynamics with virtual elements that enable realistic interactions, and employ the unique affordances of VR to augment users’ spaces. We discuss how future research in Social VR should move beyond replicating physical-world interactions and explore how immersive environments can cultivate long-term collaboration, trust, and more diverse and inclusive participation. This review highlights the current practices and challenges, highlighting new opportunities for theorizing and designing Social VR systems that responsibly support remote collaborations.
This is a corrigendum for the article "Exploring Collaboration Patterns and Strategies in Human-AI Co-creation through the Lens of Agency: A Scoping Review of the Top-tier HCI Literature" published in Proc. ACM Hum-Comput. Interact. 9, 7, Article CSCW413 (November 2025), 43 pages.
Open Source Software for Social Good (OSS4SG), a specialized segment within the Open Source Software (OSS) domain, is gaining increasing recognition for its focus on addressing societal challenges and delivering positive social impact. Learning about how contributors engage with OSS4SG is crucial to its sustainability, as the long-term success of these projects relies heavily on active and ongoing contributor participation. However, no study has yet examined the dynamics of contributors within OSS4SG. To fill this gap, we analyzed over 2.2 million commits made by 5,860 contributors to both OSS4SG and general OSS projects on GitHub, identifying contribution patterns and factors influencing sustained contribution to OSS4SG. We found that although OSS4SG contributors tend to show lower overall contribution intensity and shorter active lifespans, their activity during engaged periods is relatively more regular compared to contributions to general OSS. In addition, contributors from developing regions (e.g., Africa) or women are more likely to start with and continue contributing to OSS4SG, despite their overall contribution levels being lower than those of others. Based on these insights, we propose targeted strategies to increase contributions to OSS4SG projects to maximize their social impact to benefit society and harness their potential to foster broader participation in open source, ultimately enhancing the sustainability of the whole community.
