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We introduce a new benchmark, COVID-VTS, for fact-checking multi-modalinformation involving short-duration videos with COVID19- focused informationfrom both the real world and machine generation. We propose, TwtrDetective, aneffective model incorporating cross-media consistency checking to detecttoken-level malicious tampering in different modalities, and generateexplanations. Due to the scarcity of training data, we also develop anefficient and scalable approach to automatically generate misleading videoposts by event manipulation or adversarial matching. We investigate severalstate-of-the-art models and demonstrate the superiority of TwtrDetective.

COVID-VTS: Fact Extraction and Verification on Short Video Platforms