A large-scale dataset that consists of 21,184 claims, where each claim is assigned a truthfulness label and ruling statement, with 58,523 pieces of evidence in the form of text and images. It supports the end-to-end multimodal fact-checking and explanation generation, where the input is a claim and a large collection of web sources, including articles, images, videos, and tweets, and the goal is to assess the truthfulness of the claim by retrieving relevant evidence and predicting a truthfulness label (i.e., support, refute and not enough information), and generate a rationalization statement to explain the reasoning and ruling process.
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FACTIFY is a dataset on multi-modal fact verification. It contains images, textual claim, reference textual documenta and image. The task is to classify the claims into support, not-enough-evidence and refute categories with the help of the supporting data. We aim to combat fake news in the social media era by providing this multi-modal dataset. Factify contains 50,000 claims accompanied with 100,000 images, split into training, validation and test sets.
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