PFI-RP: A Multi-Disciplinary Approach to Detecting Adolescent Online Risks.

About This Grant

The broader impact/commercial potential of this PFI project is to develop improved social media tools to keep teens safer online. A multidisciplinary team of researchers, clinicians, and industry partners will build, evaluate, and commercialize state-of-the-art technologies that proactively detect adolescent online risk behaviors, including mental health issues, sexual solicitations, and online harassment, for the purpose of mitigating these risks and preventing harm. The team plans to release two separate technology offerings: First, an open source project that shares our risk detection algorithms with developers, so that they can build upon these solutions and form a community dedicated to promoting adolescent online safety. Second, a commercial product that combines these risk detection algorithms into an easy-to-use and accessible service that provides needed support and infrastructure to internet-based companies, who ultimately facilitate the risks teens encounter online, so that they can share in the joint responsibility of protecting the teens who use their platforms. It is imperative that as a society we become more proactive about protect teens from online risks, especially the most vulnerable teens who are at highest risk of engaging in online activities that can lead to physical harm or even death. The proposed project addresses the critical and timely problem of adolescent online safety by leveraging a multi-disciplinary approach of human-centered machine learning to accurately detect risks teen encounter online. The work will be accomplished in four stages: 1) a user-centered phase that examines the types of online risk exposure that matter most to teens and their families. With this insight, the team will build evidence-based adolescent online risk models that identify key dimensions and patterns of risk. 2) a data-driven phase that draws upon these models to establish ground truth and annotate a rich training set of teen social media data, 3) a machine-learning phase that improves existing and builds new risk detection algorithms that are contextualized to teens and able to detect behavioral changes over time, and 4) a technology development phase that evaluates our solution and commercializes it within two separate product lines. The end result will be a suite of algorithms tailored to adolescents that detect objectionable content within social media and identify problematic behavioral patterns or changes that are indicative of impending risks over time. This work will enable real-time online safety interventions that protect and empower teen internet users. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.