8월 2주차

The sharing of emotions within online healthcare communities (OHCs) is central to peer support, yet little is known about whether emotional encounters continue to shape users’ behaviors beyond the original interaction. While prior research has primarily examined immediate emotional responses within posts or discussion threads, this study investigates how emotional influence unfolds over time across a community. Drawing on a resource-based perspective, we examine two complementary mechanisms of community-level emotion flow: Emotional Resonance Cascades (ERC), whereby accumulated emotions directed at focal users in prior interactions shape their subsequent engagement and emotional expressions toward different users in later discussions, and Emotional Feedback Exchange (EFE), whereby focal users’ own emotional expressions shape the responses they subsequently receive from other community members across the network. Using relational event modeling of 65,740 reply interactions from an online HIV community, we find support for both mechanisms. Receiving positive (negative) sentiment increases (decreases) users’ reply activity and, conditional on replying, the likelihood of expressing positive sentiment in later interactions. At the same time, expressing positive (negative) sentiment increases (decreases) the likelihood of receiving replies and of those replies expressing positive sentiment. These dynamics extend beyond topics, threads, and local social clusters, and are amplified for feeling-oriented messages. A complementary experiment provides evidence consistent with a resource-based explanation by showing that receiving emotional messages alters individuals' mental resources, while observing emotional exchanges shapes perceptions of others' resourcefulness, which in turn influence individuals' behavioral tendencies toward them. Together, these findings suggest that emotional influence in OHCs extends beyond immediate emotional mirroring and provide insights into sustaining supportive digital health communities.

Regulators increasingly mandate transparency regarding generative AI (GenAI) use in creative work, aiming to protect audiences from deception while preserving creators' self-expression. One way of achieving this transparency is through disclosure labels that directly inform audiences about GenAI use. Yet, prior research focused almost exclusively on how such labels affect audience evaluations and paid surprisingly little attention to whether mandatory disclosure affects creators, too. We refer to this as the indirect disclosure effect. Drawing on Goffman's account of impression management, we theorize that creators who anticipate disclosure fear that audiences will not recognize their human creative agency, threatening their validation as a creative self, which leads them to adjust their collaboration with GenAI. To investigate this mechanism, we employ two nested mixed-methods experiments in which participants collaborate with a text-to-image GenAI tool under different disclosure conditions. We empirically establish the indirect disclosure effect: when disclosure is anticipated, the majority of creators withdraw from the creative process, leaving image generation to GenAI. We provide evidence that this withdrawal is driven by creators’ fears that audiences will not recognize their creative agency. Hence, the produced artifacts predominantly reflect computational rather than human creativity, which is also recognized and evaluated by the audience regardless of the direct disclosure label. Overall, our study reveals a fundamental tension at the heart of transparency regulation: by disclosing GenAI use through a simple label, regulators may inadvertently diminish the very human creative agency they aim to protect, and they do so before audiences ever see the label.

Physical retailers have long lacked the real-time behavioral visibility that online platforms enjoy through clickstream data. This research addresses that gap by introducing a video analytics framework that transforms in-store security camera footage into a rich, structured behavioral record: an "offline clickstream." Using computer vision and deep learning techniques, including person re-identification, trajectory reconstruction, pose estimation, and vision-language models, the system extracts moment-by-moment signals of shopper intent: how customers move through the store, how they interact with products, and how their body language evolves during a visit. A transformer-based prediction model trained on these signals achieves dramatically better purchase prediction accuracy than conventional demographic or contextual benchmarks alone: improving predictive performance by up to 79% on key metrics. Beyond prediction, the framework supports five real-time targeting policies; simulations show that a persuadability-based policy yields a 13.1% profit lift over no targeting. For retailers and policymakers, this research offers a scalable, privacy-conscious blueprint for bridging the capability gap between physical and digital commerce, enabling timely, personalized interventions that improve customer experience and store profitability.