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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. 

Online harassment negatively impacts mental health, with victims expressing increased concerns such as depression, anxiety, and even increased risk of suicide, especially among youth and young adults. Yet, research has mainly focused on building automated systems to detect harassment incidents based on publicly available social media trace data, overlooking the impact of these negative events on the victims, especially in private channels of communication. Looking to close this gap, we examine a large dataset of private message conversations from Instagram shared and annotated by youth aged 13-21. We apply trained classifiers from online mental health to analyze the impact of online harassment on indicators pertinent to mental health expressions. Through a robust causal inference design involving a difference-in-differences analysis, we show that harassment results in greater expression of mental health concerns in victims up to 14 days following the incidents, while controlling for time, seasonality, and topic of conversation. Our study provides new benchmarks to quantify how victims perceive online harassment in the immediate aftermath of when it occurs. We make social justice-centered design recommendations to support harassment victims in private networked spaces. We caution that some of the paper's content could be triggering to readers. 

With the prevalence of risks encountered by youth online, strength-based approaches such as nudges have been recommended as potential solutions to guide teens toward safer decisions. However, most nudging interventions to date have not been designed to cater to teens' unique needs and online safety concerns. To address this gap, this study provided a comprehensive view of adolescents' feedback on online safety nudges to inform the design of more effective online safety interventions. We conducted 12 semi-structured interviews and 3 focus group sessions with 21 teens (13 - 17 years old) via Zoom to get their feedback on three types of nudge designs from two opposing perspectives (i.e., risk victim and perpetrator) and for two different online risks (i.e., Information Breaches and Cyberbullying). Based on the teens' responses, they expressed a desire that nudges need to move beyond solely warning the user to providing a clear and effective action to take in response to the risk. They also identified key differences that affect the perception of nudges in effectively addressing an online risk, they include age, risk medium, risk awareness, and perceived risk severity. Finally, the teens identified several challenges with nudges such as them being easy to ignore, disruptive, untimely, and possibly escalating the risk. To address these, teens recommended clearer and contextualized warnings, risk prevention, and nudge personalization as solutions to ensure effective nudging. Overall, we recommend online safety nudges be designed for victim guidance while providing autonomy to control their experiences, and to ensure accountability and prevention of risk perpetrators to restrict them from causing harm.