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 …

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 …

We collected Instagram data from 150 adolescents (ages 13-21) that included 15,547
private message conversations of which 326 conversations were flagged as sexually risky …

Instagram, one of the most popular social media platforms among youth, has recently come
under scrutiny for potentially being harmful to the safety and well-being of our younger …

Computational approaches to detect the online risks that the youth encounter have
presented promising potentials to protect them online. However, a major identified trend …

There is growing concern regarding adolescent online risks posed by social media. Prior
work calls for a paradigm shift from restrictive approaches towards strength-based solutions …

We conducted an ethnographically-informed study with 28 participants (9 autistic Young
Adults or" YAs" in need of substantial daily support, 6 parents, 13 support staff) to …

A majority of people experience trauma, spurring calls to incorporate trauma-informed
approaches (TIA) from public health and social work into technology design. While …

Sexual risk detection AI has been touted as a scalable solution for computer-mediated
sexual violence. Data donation is a user-centered approach to producing ecologically valid …

In this position paper, we discuss the paradigm shift that has emerged in the literature,
suggesting to move away from restrictive and authoritarian parental mediation approaches …