Addressing the threats of disinformation

Addressing the threats of disinformation
Data analytics
Past Projects

Overview

Towards a computational framework for disinformation trinity: heterogeneity, generation, and explanation

Foreign influence exists in many methodologies and forms, including efforts to spread disinformation. These activities often cause serious societal issues, eroding public trust in everything from longstanding institutions to current elections. Disinformation not only increases the polarization of society, it also causes economic loss through the theft of intellectual property and trade secrets.

This project will develop a comprehensive computational framework for modeling foreign influence disinformation efforts on the news media. These tools will be designed to strengthen national defenses against foreign influence operations.

Solution

To create these tools, the team will research the life cycle of disinformation. This includes learning more about the space where it embeds, including ways it generates and evolves. Data collected will help in the creation of computational algorithms and software tools to detect, predict, and explain disinformation. This will empower leaders to reduce and even eliminate this type of foreign influence operations and their harmful events.

Impact

This suite of algorithms and software tools promises to mitigate the risks of foreign influence on our government and society. With these new assets, officials will be able to identify messaging, tactics, target audience, and outreach. In turn, this will help to detect, predict and explain disinformation more accurately and comprehensively, even when engaging simultaneous operations on multiple fronts. Possible ongoing applications include assisting with CISA-National Risk Management Center initiatives, including the defense of fair and free elections against the influence of foreign governments.

Research Leadership Team

Principal Investigator: Jingrui He, University of Illinois, Urbana-Champaign
Co-PI: Hanghang Tong, University of Illinois, Urbana-Champaign
Co-PI: Ross Maciejewski, Arizona State University