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The internet gives the world an open platform to express their views andshare their stories. While this is very valuable, it makes fake news one of oursociety's most pressing problems. Manual fact checking process is timeconsuming, which makes it challenging to disprove misleading assertions beforethey cause significant harm. This is he driving interest in automatic fact orclaim verification. Some of the existing datasets aim to support development ofautomating fact-checking techniques, however, most of them are text based.Multi-modal fact verification has received relatively scant attention. In thispaper, we provide a multi-modal fact-checking dataset called FACTIFY 2,improving Factify 1 by using new data sources and adding satire articles.Factify 2 has 50,000 new data instances. Similar to FACTIFY 1.0, we have threebroad categories - support, no-evidence, and refute, with sub-categories basedon the entailment of visual and textual data. We also provide a BERT and VisonTransformer based baseline, which achieves 65% F1 score in the test set. Thebaseline codes and the dataset will be made available athttps://github.com/surya1701/Factify-2.0.

Factify 2: A Multimodal Fake News and Satire News Dataset