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

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

URL: http://arxiv.org/abs/2203.07990v1
Date: Fri, 28 Jan 2022 18:13:03 GMT
Title: UofA-Truth at Factify 2022 : Transformer And Transfer Learning Based Multi-Modal Fact-Checking
Authors: Abhishek Dhankar, Osmar R. Za\"iane and Francois Bolduc
Abstract summary: We attempted to tackle the problem of automated misinformation/disinformation detection in multi-modal news sources. Our model produced an F1-weighted score of 74.807%, which was the fourth best out of all the submissions.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
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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