Screen use pervades daily life, shaping work, leisure, and social connections while raising concerns for digital wellbeing. Yet, reducing screen time alone risks oversimplifying technology’s role and neglecting its potential for meaningful engagement. We posit self-awareness---reflecting on one’s digital behavior---as a critical pathway to digital wellbeing. We developed WellScreen, a lightweight probe that scaffolds daily reflection by asking people to estimate and report smartphone use. In a two-week deployment with college students (N=25) focused on generating formative insights, we examined how discrepancies between estimated and actual usage shaped digital awareness and wellbeing. Participants often underestimated productivity and social media while overestimating entertainment app use. They showed a 10% improvement in positive affect, rating WellScreen as moderately useful. Interviews revealed that structured reflection supported recognition of patterns, adjustment of expectations, and more intentional engagement with technology. Our findings highlight the promise of lightweight reflective interventions for supporting self-awareness and intentional digital engagement, offering implications for designing digital wellbeing tools.
We often treat social media as a lens onto society. How might that lens distort the popularity of political and social viewpoints? We examine discrepancies between publicly posted and privately surveyed opinions within communities, contributing a measurement of the "spiral of silence'' theory; the theory posits people are less likely to voice opinions when they believe they hold minority views, creating a reinforcing cycle where these opinions are expressed less. We surveyed members of politically-oriented Reddit communities about their willingness to post on contentious topics, yielding 439 responses across twelve subreddits. 72.1% of participants who perceive themselves in the minority remain silent and are half as likely to post compared to those who believe their opinion is in the majority. Community design factors, such as perceived diversity, are associated with less self-silencing. We provide recommendations for counteracting self-silencing at the community level (e.g., positive reinforcement, more transparent moderation). Overall, these results reveal gaps between online discourse and broader public opinion.
While family informatics has been developed for monitoring and tracking family-centered health data, there remains a gap in understanding how family informatics can support families in reflecting on their social behaviors and emotional dynamics. We address this gap with SELaD, a system that captures and visualizes social-emotional data from daily family interactions using audio, video, and physiological sensors. In a semi-naturalistic study with 17 families ($n=51$), we investigated how this data facilitates reflection. Our findings reveal a process we term \emph{relational reflection}, where families collaboratively interpret multimodal data to deepen their understanding of conversational dynamics and emotional influences by recalling their shared history and expectation of good communication. This process was particularly enriched by emotional data from multiple sources that families could cross-reference and reconcile. This work presents SELaD as a technology probe and empirically grounds the concept of relational reflection, positioning it as a foundation for designing future reflective technologies.
We present ViSTAR, a Virtual Skill Training system in AR that supports self-guided basketball skill practice, with feedback on balance, posture, and timing. From a formative study with basketball players and coaches, the system addresses three challenges: understanding skills, identifying errors, and correcting mistakes. ViSTAR follows the Behavioral Skills Training (BST) framework—instruction, modeling, rehearsal, and feedback. It provides feedback through visual overlays, rhythm and timing cues, and an AI-powered coaching agent using 3D motion reconstruction. We generate verbal feedback by analyzing spatio-temporal joint data and mapping features to natural-language coaching cues via a Large Language Model (LLM). A key novelty is this feedback generation: motion features become concise coaching insights. In two studies (N=16), participants generally preferred our AI-generated feedback to coach feedback and reported that ViSTAR helped them notice posture and balance issues and refine movements beyond self-observation.
Cybergrooming is a form of online abuse that threatens teens' mental health and physical safety. Yet, most prior work has focused on detecting perpetrators’ behaviors, leaving a limited understanding of how teens might respond to such unwanted advances. To address this gap, we conducted an online survey with 74 participants---51 parents and 23 teens---who responded to simulated cybergrooming scenarios in two ways: responses that they think would make teens more vulnerable or resilient to unwanted sexual advances. Through a mixed-methods analysis, we identified four types of vulnerable responses (encouraging escalation, accepting an advance, displaying vulnerability, and negating risk concern) and four types of protective strategies (setting boundaries, directly declining, signaling risk awareness, and leveraging avoidance techniques). As the cybergrooming risk escalated, both vulnerable responses and protective strategies showed a corresponding progression. This study contributes a teen-centered understanding of cybergrooming, a labeled dataset, and a stage-based taxonomy of perceived protective strategies, while offering implications for educational programs and sociotechnical interventions.
Social media dependency is a central mechanism through which digital vulnerability takes shape, making it critical to understand for research, design, and policy. This study distinguishes between functional dependency (needs-based reliance) and psychological dependency (compulsive engagement) and investigates how these dimensions intersect. We surveyed 873 adult users across Europe, measuring both dependency forms alongside demographics, well-being, motivations, platform choice, and exposure to manipulative design features. Latent profile analysis and multinomial logistic regression revealed five distinct dependency profiles: functional use, low-dependency pragmatic use, high-dependency social use, moderate-dependency hedonic use, and very high-dependency multi-motivated use. These findings show dependency is not uniform but layered and dynamic, shifting with users’ circumstances and socio-technical contexts. By situating dependency within both individual and design-related factors, the study advances theoretical debates on digital vulnerability and offers a profiles-based lens that helps inform the design of more autonomy-supportive social media platforms.
People often have things they want to say but hold back in conversations, fearing being vulnerable or facing social consequences. Online, this restraint can take a distinctive form: even when such thoughts are written out - in moments of anger, guilt, or longing - people may choose to withhold them, leaving them unsent. This process is underexamined; we investigate the experience of writing such messages within people's digital communications. We find that unsent messages become expressive containers for suppressed feelings, where the act of writing creates a pause for reflection on the relationship and oneself. Building on these insights, we probed into how the design of the writing platforms of unsent messages affects people's experiences and motivations. Speculating with participants on nine evocative variants of a note-taking platform, we highlight how design shapes the emotional, temporal, and ritualistic qualities of unsent messages, revealing tensions between people's social desires and communicative actions.