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UofA-Truth at Factify 2022 : Transformer And Transfer Learning Based Multi-Modal Fact-Checking

2022/01/28 by Abhishek Dhankar, Dhankar, Abhishek, Osmar R. Zaïane +3
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Multimedia (cs.MM) #cs.AI #cs.CL #cs.MM

paper · pdf · doi:10.48550/arxiv.2203.07990

arxiv created 2022/01/28 · arxiv updated 2022/03/16

Abstract

Identifying fake news is a very difficult task, especially when considering the multiple modes of conveying information through text, image, video and/or audio. We attempted to tackle the problem of automated misinformation/disinformation detection in multi-modal news sources (including text and images) through our simple, yet effective, approach in the FACTIFY shared task at De-Factify@AAAI2022. Our model produced an F1-weighted score of 74.807%, which was the fourth best out of all the submissions. In this paper we will explain our approach to undertake the shared task.

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