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Combating disinformation is one of the burning societal crises -- about 67%of the American population believes that disinformation produces a lot ofuncertainty, and 10% of them knowingly propagate disinformation. Evidence showsthat disinformation can manipulate democratic processes and public opinion,causing disruption in the share market, panic and anxiety in society, and evendeath during crises. Therefore, disinformation should be identified promptlyand, if possible, mitigated. With approximately 3.2 billion images and 720,000hours of video shared online daily on social media platforms, scalabledetection of multimodal disinformation requires efficient fact verification.Despite progress in automatic text-based fact verification (e.g., FEVER, LIAR),the research community lacks substantial effort in multimodal factverification. To address this gap, we introduce FACTIFY 3M, a dataset of 3million samples that pushes the boundaries of the domain of fact verificationvia a multimodal fake news dataset, in addition to offering explainabilitythrough the concept of 5W question-answering. Salient features of the datasetinclude: (i) textual claims, (ii) ChatGPT-generated paraphrased claims, (iii)associated images, (iv) stable diffusion-generated additional images (i.e.,visual paraphrases), (v) pixel-level image heatmap to foster image-textexplainability of the claim, (vi) 5W QA pairs, and (vii) adversarial fake newsstories.

FACTIFY3M: A Benchmark for Multimodal Fact Verification with Explainability through 5W Question-Answering