Computational Fact-checking
Journalists cannot read every transcript in full. We build resources that help, from a benchmark dataset of check-worthy factual claims to ClaimViz, which we believe is the first visual analytics system for verifying claims in spoken text such as debates, speeches and interviews.
ClaimViz lets fact-checkers filter a long transcript down to its most check-worthy sentences, then find evidence that supports or contradicts each claim. Recent work surveys information disorder on video-sharing platforms and detects misinformation that combines text, images and video.



ResourcesPresidential debate claims dataset
Publications
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arXiv 2026
MOMENTA: Mixture-of-Experts Over Multimodal Embeddings with Neural Temporal Aggregation for Misinformation Detection
arXiv preprint arXiv:2604.16172
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CBMI 2025
A Survey of Information Disorder on Video-Sharing Platforms
2025 International Conference on Content-Based Multimedia Indexing (CBMI)
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IEEE VIS 2020
ClaimViz: Visual Analytics for Identifying and Verifying Factual Claims
2020 IEEE Visualization Conference (VIS)
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arXiv 2020
Towards Domain-Specific Characterization of Misinformation
arXiv preprint arXiv:2007.14806
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ICWSM 2020
A Benchmark Dataset of Check-Worthy Factual Claims
Proceedings of the International AAAI Conference on Web and Social Media
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JDIQ 2019
Introduction to the Special Issue on Combating Digital Misinformation and Disinformation
Journal of Data and Information Quality (JDIQ)