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Research involving sensitive data often leads to valuable human-centered insights. Yet, the effects of participating in and conducting research about sensitive data with youth are poorly understood. We conducted meta-level research to improve our understanding of these effects. We did the following: (i) asked youth (aged 13-21) to share their private Instagram Direct Messages (DMs) and flag their unsafe DMs; (ii) interviewed 30 participants about the experience of reflecting on this sensitive data; (iii) interviewed research assistants (RAs, n=12) about their experience analyzing youth's data. We found that reflecting about DMs brought discomfort for participants and RAs, although both benefited from increasing their awareness about online risks, their behavior, and privacy and social media practices. Participants had high expectations for safeguarding their private data while their concerns were mitigated by the potential to improve online safety. We provide implications for ethical research practices and the development of reflective practices among participants and RAs through applying trauma-informed principles to HCI research. 

Social service providers play a vital role in the developmental outcomes of underprivileged youth as they transition into adulthood. Educators, mental health professionals, juvenile justice officers, and child welfare caseworkers often have first-hand knowledge of the trials uniquely faced by these vulnerable youth and are charged with mitigating harmful risks, such as mental health challenges, child abuse, drug use, and sex trafficking. Yet, less is known about whether or how social service providers assess and mitigate the online risk experiences of youth under their care. Therefore, as part of the National Science Foundation (NSF) I-Corps program, we conducted interviews with 37 social service providers (SSPs) who work with underprivileged youth to determine what (if any) online risks are most concerning to them given their role in youth protection, how they assess or become aware of these online risk experiences, and whether they see value in the possibility of using artificial intelligence (AI) as a potential solution for online risk detection. Overall, online sexual risks (e.g., sexual grooming and abuse) and cyberbullying were the most salient concern across all social service domains, especially when these experiences crossed the boundary between the digital and the physical worlds. Yet, SSPs had to rely heavily on youth self-reports to know whether and when online risks occurred, which required building a trusting relationship with youth; otherwise, SSPs became aware only after a formal investigation had been launched. Therefore, most SSPs found value in the potential for using AI as an early detection system and to monitor youth, but they were concerned that such a solution would not be feasible due to a lack of resources to adequately respond to online incidences, access to the necessary digital trace data (e.g., social media), context, and concerns about violating the trust relationships they built with youth. Thus, such automated risk detection systems should be designed and deployed with caution, as their implementation could cause youth to mistrust adults, thereby limiting the receipt of necessary guidance and support. We add to the bodies of research on adolescent online safety and the benefits and challenges of leveraging algorithmic systems in the public sector. 

Sexual exploration is a natural part of adolescent development; yet, unmediated internet access has enabled teens to engage in a wider variety of potentially riskier sexual interactions than previous generations, from normatively appropriate sexual interactions to sexually abusive situations. Teens have turned to online peer support platforms to disclose and seek support about these experiences. Therefore, we analyzed posts (N=45,955) made by adolescents (ages 13--17) on an online peer support platform to deeply examine their online sexual risk experiences. By applying a mixed methods approach, we 1) accurately (average of AUC = 0.90) identified posts that contained teen disclosures about online sexual risk experiences and classified the posts based on level of consent (i.e., consensual, non-consensual, sexual abuse) and relationship type (i.e., stranger, dating/friend, family) between the teen and the person in which they shared the sexual experience, 2) detected statistically significant differences in the proportions of posts based on these dimensions, and 3) further unpacked the nuance in how these online sexual risk experiences were typically characterized in the posts. Teens were significantly more likely to engage in consensual sexting with friends/dating partners; unwanted solicitations were more likely from strangers and sexual abuse was more likely when a family member was involved. We contribute to the HCI and CSCW literature around youth online sexual risk experiences by moving beyond the false dichotomy of "safe" versus "risky". Our work provides a deeper understanding of technology-mediated adolescent sexual behaviors from the perspectives of sexual well-being, risk detection, and the prevention of online sexual violence toward youth. 

In this work, we present a case study on an Instagram Data Donation (IGDD) project, which is a user study and web-based platform for youth (ages 13-21) to donate and annotate their Instagram data with the goal of improving adolescent online safety. We employed human-centered design principles to create an ecologically valid dataset that will be utilized to provide insights from teens’ private social media interactions and train machine learning models to detect online risks. Our work provides practical insights and implications for Human-Computer Interaction (HCI) researchers that collect and study social media data to address sensitive problems relating to societal good.