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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.