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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. 

ACM Conference on Human Factors in Computing Systems 

Accurate real-time risk identification is vital to protecting social media users from online harm, which has driven research towards advancements in machine learning (ML). While strides have been made regarding the computational facets of algorithms for “real-time” risk detection, such research has not yet evaluated these advancements through a human-centered lens. To this end, we conducted a systematic literature review of 53 peer-reviewed articles on real-time risk detection on social media. Real-time detection was mainly operationalized as “early” detection after-the-fact based on pre-defined chunks of data and evaluated based on standard performance metrics, such as timeliness. We identified several human-centered opportunities for advancing current algorithms, such as integrating human insight in feature selection, algorithms’ improvement considering human behavior, and utilizing human evaluations. This work serves as a critical call-to-action for the HCI and ML communities to work together to protect social media users before, during, and after exposure to risks. 

Recent increases in self-harm and suicide rates among youth have coincided with prevalent social media use; therefore, making these sensitive topics of critical importance to the HCI research community. We analyzed 1,224 direct message conversations (DMs) from 151 young Instagram users (ages 13-21), who engaged in private conversations using self-harm and suicide-related language. We found that youth discussed their personal experiences, including imminent thoughts of suicide and/or self-harm, as well as their past attempts and recovery. They gossiped about others, including complaining about triggering content and coercive threats of self-harm and suicide but also tried to intervene when a friend was in danger. Most of the conversations involved suicide or self-harm language that did not indicate the intent to harm but instead used hyperbolical language or humor. Our results shed light on youth perceptions, norms, and experiences of self-harm and suicide to inform future efforts towards risk detection and prevention. ContentWarning: This paper discusses the sensitive topics of self-harm and suicide. Reader discretion is advised. 

ACM Conference on Human Factors in Computing Systems 

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. 

We conducted a study with 173 adolescents (ages 13-21), who self-reported their offline and online risk experiences and uploaded their Instagram data to our study website to flag private conversations as unsafe. Risk profiles were first created based on the survey data and then compared with the risk-flagged social media data. Five risk profiles emerged: Low Risks (51% of the participants), Medium Risks (29%), Increased Sexting (8%), Increased Self-Harm (8%), and High Risk Perpetration (4%). Overall, the profiles correlated well with the social media data with the highest level of risk occurring in the three smallest profiles. Youth who experienced increased sexting and self-harm frequently reported engaging in unsafe sexual conversations. Meanwhile, high risk perpetration was characterized by increased violence, threats, and sales/promotion of illegal activities. A key insight from our study was that offline risk behavior sometimes manifested differently in online contexts (i.e., offline self-harm as risky online sexual interactions). Our findings highlight the need for targeted risk prevention strategies for youth online safety. 

Artificial intelligence (AI) underpins virtually every experience that we have—from search and social media to generative AI and immersive social virtual reality (SVR). For Generation Z, there is no before AI. As adults, we must humble ourselves to the notion that AI is shaping youths’ world in ways that we don’t understand and we need to listen to them about their lived experiences. We invite researchers from academia and industry to participate in a workshop with youth activists to set the agenda for research into how AI-driven emerging technologies affect youth and how to address these challenges. This reflective workshop will amplify youth voices and empower youth and researchers to set an agenda. As part of the workshop, youth activists will participate in a panel and steer the conversation around the agenda for future research. All will participate in group research agenda setting activities to reflect on their experiences with AI technologies and consider ways to tackle these challenges. 

Although youth increasingly communicate with peers online, we know little about how private online channels play a role in providing a supportive environment for youth. To fill this gap, we asked youth to donate their Instagram Direct Messages and filtered them by the phrase “help me.” From this query, we analyzed 82 conversations comprised of 336,760 messages that 42 participants donated. These threads often began as casual conversations among friends or lovers they met offline or online. The conversations evolved into sharing negative experiences about everyday stress (e.g., school, dating) to severe mental health disclosures (e.g., suicide). Disclosures were usually reciprocated with relatable experiences and positive peer support. We also discovered unsupport as a theme, where conversation members denied giving support, a unique finding in the online social support literature. We discuss the role of social media-based private channels and their implications for design in supporting youth’s mental health. Content Warning: This paper includes sensitive topics, including self-harm and suicide ideation. Reader discretion is advised.