I-Corps: Risk Detection Algorithms for Adolescent Online Safety

The University Of Central Florida Board Of Trustees, Orlando FL

Investigators

Abstract

The broader impact/commercial potential of this I-Corps project is that it will greatly benefit society by serving to proactively protect youth from serious online risks. A multidisciplinary team of researchers, clinicians, and industry partners will build, evaluate, and explore the commercial potential for state-of-the-art machine learning algorithms that detect adolescent online risk behaviors online, including mental health issues, sexual solicitations, and online harassment, for the purpose of mitigating these risks and preventing harm. The project will explore the release the algorithms as 1) an open source project that creates a community dedicated to promoting adolescent online safety, and 2) a software as a service application programming interface that makes this solution publicly available to social media and internet-based companies. The intent is to shift responsibility away from relying on third-party parental control software for protecting youth to directly enabling online platforms that cater to youth to play a larger role in protecting teens. By serving the direct needs of these customers, this project will also provide secondary benefits to teens and their families by proactively protecting teens from online risks. This I-Corps project will further develop a suite of algorithms tailored to adolescents that not only detect objectionable content within social media data but can also identify problematic behavioral patterns or changes that are indicative of impending risks over time. The intellectual merit of this work lies in the novel and intertwined synthesis of multi-modal data, machine learning techniques and human-centered approaches, in concert with clinical and industrial partnerships, for the design and development of tools that address the problem of adolescent online risk detection. This approach is transformative because it relies on developing a deeper contextual understanding of teen social media users by leveraging human insight and meta-level data about the social media content, rather than analyzing the social media data by itself. 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.