EAGER: DCL: SaTC: EIC: Inclusive-ScamBuster: Inclusive Scam Detection Methods for Social Media to Design Assistive Tools for Protecting Individuals with Developmental Disabilities

George Mason University, Fairfax VA

Investigators

Abstract

Preventing social media-based scams is a critical challenge for cybersecurity. There exist tools to protect individuals during online browsing, however, they are not tailored towards vulnerable subpopulations like individuals with developmental disabilities (e.g., Autism). Such individuals become targets without dedicated support to assist with threat identification in potential scam posts. This project aims to understand the distinctive comprehension and attention patterns displayed by individuals with Autism and Attention-Deficit/Hyperactivity Disorder (ADHD), to improve scam detection tools to assist these subpopulations. The project’s novelties include a multidisciplinary approach combining social computing, cognitive psychology, special education, and computational linguistics research to address existing biases in Artificial Intelligence methods of Natural Language Processing (NLP) used in scam detection tools, based on behavioral studies of browsing patterns displayed by vulnerable subpopulations. The project’s broader significance is in integrating insights of human behavior into cybersecurity tools, leading to better protection of vulnerable subpopulations and greater inclusiveness in cybersecurity. This project pursues two goals. First, it develops an eye-tracking study to discover variations in attention patterns observable across populations with and without developmental disabilities when exposed to scams and legitimate social media posts. Second, it uses observed variations in attention patterns to highlight representation biases in the labeled datasets of NLP-based scam detection models. It further creates a novel set of linguistic attributes that can be used to train scam detection models tailored to aid vulnerable subpopulations. Project outcomes include a better understanding of social media scams for vulnerable subpopulations, the development of an inclusive NLP model for scam detection, and an open-source browser plugin prototype to aid individuals with developmental disabilities via tailored scam alerts. The project also creates a web portal (Inclusive-ScamBuster) hosting labeled scam datasets to highlight representational biases and open-source educational resources to support Special Education programs in teaching and training cybercrime prevention. 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.