ABSTRACT

Fact-checking can be effective in countering the growing threat of online misinformation because people across the political spectrum and demographics tend to trust credibility judgments of fact-checkers. However, a pipeline of manual and labor-intensive practices fragmented across disparate tools makes it difficult to scale fact-checking efforts. As a result, fact-checkers are inundated with information and lack effective dissemination mechanisms for countering misinformation early and effectively. To address these challenges, this project combines the complementary information processing strengths of humans and computation to transform the efficiency, effectiveness, and scale of fact-checking. The project can enable fact-checkers to spot misinformation early, prioritize effort, and unify the various tools and techniques used for fact-checking. The research outcomes can scale the work of human fact-checkers and boost information literacy in society, which can significantly reduce the number of people exposed to misinformation.

The project draws upon the core components of security incident response (i.e., preparation, detection, containment, and post-incident activity) to transform the ad-hoc, time-consuming, and small-scale nature of current fact-checking practices with a security-analyst perspective and a unified user experience (UX). The research approach leverages the power of computation and personalization while retaining the synergistic advantages of the human fact-checker in the loop. The interdisciplinary sociotechnical approach involves empirical studies of fact-checker practices, collection of data and development of computational techniques to address their challenges and barriers, and design explorations of novel UI/UX techniques to connect humans and computation. The research incorporates a feedback loop to disseminate fact-checking outcomes, thus boosting their visibility and impact on end users exposed to misinformation. The researchers are developing early warning and detection techniques to reduce the time between misinformation generation and fact-check dissemination and are employing prioritization and personalization for more effective and efficient use of fact-checking resources. The researchers are engaging with professional fact-checkers to translate the research outcomes to the real world.

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.

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