Social Media Data for Online Adolescent Suicide Risk Identification: Considerations for Integration Within Platforms, Clinics, and Schools

Abstract

This chapter is designed to discuss the opportunities to use adolescent-generated social media data in order to detect and monitor behavior patterns predictive of risk for suicide. First, current suicide prevention strategies are outlined. Next, the rationale for utilization of social media is considered given that many adolescents use social media platforms. Aggregating social media data for use with machine learning strategies can provide the basis for a “virtual gatekeeper” that may augment communities’ ability to remain continually awake to suicide risk detection. Several approaches have been developed but are yet to be evaluated beyond a proof of concept phase. Three different existing digital technologies designed to apply machine learning algorithms to social media data to improve identification and management of suicide risk are presented and discussed. Finally, we consider strategies for their integration in the settings which adolescents commonly seek guidance and healthcare. Future research is needed to examine the extent to which adolescent-generated social media data can provide the information needed to accurately predict and reduce suicide risk.