Hwajung Hong

As digital content consumption becomes increasingly personalized, post-viewing activities are shifting online. After watching a film, users actively generate reviews, sharing their appreciation and exploring others’. We introduce CineTalk, an LLM-based chatbot grounded in diverse film reviews, and examine user experiences across three interaction modes—Agreeing, Disagreeing, and Unexpected—defined by how each chatbot aligns with users’ stances and discussion topics. Through a user study with 18 participants, we found CineTalk made exploring diverse perspectives more convenient and interactive. We also identified users’ expectations and concerns about the chatbot’s review reconstruction, calling for further research. Finally, our findings suggest that integrating the three modes within a single chatbot through the reciprocal exchange—beginning with user-led AgreeingDisagreeing interactions and smoothly shifting to chatbot-led Unexpected prompts when conversations stagnate—may enable a balanced exploration of diverse perspectives. This study offers preliminary insights into how review-grounded LLM chatbots support more enriched post-viewing experiences with films.

Sensing-based stress management systems integrate self-report, physiological, and contextual data to assess users' perceived stress and physiological indicators related to stress responses and deliver just-in-time interventions. Prior work has largely focused on improving detection accuracy or evaluating intervention effectiveness, with relatively limited attention to how users interpret and make sense of these data in everyday work contexts. Our study addresses this gap by examining how emotional labor workers construct meaning around alignment and divergence between perceived stress and physiological responses to stress. We present a four-week in-the-wild study with 19 call center workers, combining mobile and wearable sensing, a reflection dashboard, and in-depth interviews. Our findings show that participants interpreted stress indicators through work context, bodily conditions, and prior experiences, and treated divergence as an informative cue rather than a simple error. They also recognized that perceived and physiological responses to stress could change differently before and after micro-interventions. Based on these findings, we discuss design implications for sensing-based stress management systems that support stress data literacy and flexible, context-grounded stress sensemaking in practice.

Investment decisions are often driven by time pressure and emotion, leaving investors vulnerable to cognitive biases. Mobile trading apps intensify these tendencies, yet existing interventions rely on external constraints that fail to foster lasting behavioral change. We investigate how reflection-centered approaches support mindful decision-making across a spectrum of investor expertise. We present MindStock, a technology probe providing principle-anchored feedback by integrating user-defined principles with behavioral data mirroring trading patterns. In a 6-week field study with 16 investors, we found that meaningful reflection comes from the tension between principles and behavioral data. Principles give context to otherwise opaque metrics, while data keeps principles from drifting into vague self-assurances. This pattern varied by experience: novices gravitated toward normative rule-setting, while experienced investors used the system to test and refine their own assumptions. We contribute design implications for supporting reflection in high-stakes decision-making contexts.

Social media clones are AI-powered social delegates of ourselves created using our personal data. As our identities and online personas intertwine, these technologies have the potential to greatly enhance our social media experience. If mismanaged however, these clones may also pose new risks to our social reputation and online relationships. To set the foundation for a productive and responsible integration, we set out to understand how social media clones will impact our online behavior and interactions. We conducted a series of semi-structured interviews introducing eight speculative clone concepts to 32 social media users through a design workbook. Applying existing work in AI-mediated communication in the context of social media, we found that although clones can offer convenience and comfort, they can also threaten the user’s authenticity and increase distrust within the online community. As a result users tend to behave more like their clones to mitigate discrepancies and interaction breakdowns. These findings are discussed through the lens of past literature in identity and impression management to highlight challenges in the adoption of social media clones by the general public, and propose design considerations for their successful integration into social media platforms.