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On social media, teens must manage their interpersonal boundaries not only with other people, but also with the algorithms embedded in these platforms. In this context, we engaged seven teens in an Asynchronous Remote Community (ARC) as part of a multi-year Youth Advisory Board (YAB) to discuss how they navigate, cope, and co-design for improved boundary management. Teens had preconceived notions of different platforms and navigated boundaries based on specific goals; yet, they struggled when platforms lacked the granular controls needed to meet their needs. Teens enjoyed the personalization afforded by algorithms, but they felt violated when algorithms pushed unwanted content. Teens designed features for enhanced control over their discoverability and for real-time risk detection to avoid boundary turbulence. We provide design guidelines for improved social media boundary management for youth and pinpoint educational opportunities to enhance teens’ understanding and use of social media privacy settings and algorithms. 

Ensuring the online safety of youth has motivated research towards the development of machine learning (ML) methods capable of accurately detecting social media risks after-the-fact. However, for these detection models to be effective, they must proactively identify high-risk scenarios (e.g., sexual solicitations, cyberbullying) to mitigate harm. This `real-time' responsiveness is a recognized challenge within the risk detection literature. Therefore, this paper presents a novel two-level framework that first uses reinforcement learning to identify conversation stop points to prioritize messages for evaluation. Then, we optimize state-of-the-art deep learning models to accurately categorize risk priority (low, high). We apply this framework to a time-based simulation using a rich dataset of 23K private conversations with over 7 million messages donated by 194 youth (ages 13-21). We conducted an experiment comparing our new approach to a traditional conversation-level baseline. We found that the timeliness of conversations significantly improved from over 2 hours to approximately 16 minutes with only a slight reduction in accuracy (0.88 to 0.84). This study advances real-time detection approaches for social media data and provides a benchmark for future training reinforcement learning that prioritizes the timeliness of classifying high-risk conversations. 

In the smart home landscape, there is an increasing trend of homeowners sharing device access outside their homes. This practice presents unique challenges in terms of security and privacy. In this study, we evaluated the co-management features in smart home management systems to investigate 1) how homeowners establish and authenticate shared users’ access, 2) the access control mechanisms, and 3) the management, monitoring, and revocation of access for shared devices. We conducted a systematic feature analysis of 11 Android and iOS mobile applications (“apps”) and 2 open-source platforms designed for smart home management. Our study revealed that most smart home systems adopt a centralized control model which necessitates shared users to utilize the primary app for device access, while providing diverse sharing mechanisms, such as email or phone invitations and unique codes, each presenting distinct security and privacy advantages. Moreover, we discovered a variety of access control options, ranging from full access to granular access control such as time-based restrictions which, while enhancing security and convenience, necessitate careful management to avoid user confusion. Additionally, our findings highlighted the prevalence of comprehensive methods for monitoring shared users’ access, with most systems providing detailed logs for added transparency and security, although there are some restrictions to safeguard homeowner privacy. Based on our findings, we recommend enhanced access control features to improve user experience in shared settings. 

The debate on whether social media has a net positive or negative effect on youth is ongoing. Therefore, we conducted a thematic analysis on 2,061 posts made by 1,038 adolescents aged 15-17 on an online peer-support platform to investigate the ways in which these teens discussed popular social media platforms in their posts and to identify differences in their experiences across platforms. Our findings revealed four main emergent themes for the ways in which social media was discussed: 1) Sharing negative experiences or outcomes of social media use (58%, n = 1,095), 2) Attempts to connect with others (45%, n = 922), 3) Highlighting the positive side of social media use (20%, n = 409), and 4) Seeking information (20%, n = 491). Overall, while sharing about negative experiences was more prominent, teens also discussed balanced perspectives of connection-seeking, positive experiences, and information support on social media that should not be discounted. Moreover, we found statistical significance for how these experiences differed across social media platforms. For instance, teens were most likely to seek romantic relationships on Snapchat and self-promote on YouTube. Meanwhile, Instagram was mentioned most frequently for body shaming, and Facebook was the most commonly discussed platform for privacy violations (mostly from parents). The key takeaway from our study is that the benefits and drawbacks of teens' social media usage can co-exist and net effects (positive or negative) can vary across different teens across various contexts. As such, we advocate for mitigating the negative experiences and outcomes of social media use as voiced by teens, to improve, rather than limit or restrict, their overall social media experience. We do this by taking an affordance perspective that aims to promote the digital well-being and online safety of youth by design. 

Traditional online safety technologies often overly restrict teens and invade their privacy, while parents often lack knowledge regarding their digital privacy. As such, prior researchers have called for more collaborative approaches on adolescent online safety and networked privacy. In this paper, we propose family-centered approaches to foster parent-teen collaboration in ensuring their mobile privacy and online safety while respecting individual privacy, to enhance open discussion and teens' self-regulation. However, challenges such as power imbalances and conflicts with family values arise when implementing such approaches, making parent-teen collaboration difficult. Therefore, attending the family-centered design workshop provided an invaluable opportunity for us to discuss these challenges and identify best research practices for the future of collaborative online safety and privacy within families.