NSF Award
2227488
Cyberbullying, a common form of online misbehavior, can do serious psychological and physical harm. This project integrates computer science and psychology frameworks to advance our understanding of how cyberbullying can be identified and prevented. The core objective of this interdisciplinary project is to design and develop models and apps to prevent and identify instances of cyberbullying in social networks. It uses psychological theory to guide investigations of the nature of cyberbullying and the adoption of automated anti-bullying tools, as well as carefully-designed data collection processes and evaluation frameworks. The project's impact includes sharing research and educational resources, raising cyberbullying awareness with policy-makers, and providing graduate and undergraduate students with the scientific scaffolding to develop into recognized interdisciplinary scholars. <br/><br/>The intellectual merit of this project stems from its synergistic integration of computer and psychological sciences to address a major social problem. This project studies automated models for cyberbullying detection that leverage a large body of relevant empirical work in psychology and seeks to develop evidence-based tools for identifying and preventing cyberbullying. It integrates innovative machine learning models for cyberbullying identification (including personalized, temporal-analysis-based, multi-modal, and privacy-preserving models) with the development of evidence-based apps for identifying and addressing cyberbullying (e.g., apps that strengthen parent-teen relationships within the context of cyberbullying and social media). The project also uses systematically-annotated datasets and survey methods to identify mechanisms to enhance the use and usability of anti-cyberbullying technologies and investigate key aspects of the nature of cyberbullying, including temporal characteristics of cyberbullying and connections between social media use and psychological correlates of cyberbullying.<br/><br/>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.
