UofA-Truth at Factify 2022 : Transformer And Transfer Learning Based Multi-Modal Fact-Checking
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 28 Jan 2022.
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References · 6
- 2015Distilling the Knowledge in a Neural Networkcited by 10,324
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
- 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
- 2018A Retrospective Analysis of the Fake News Challenge Stance Detection Taskcited by 49
- 2017A simple but tough-to-beat baseline for the Fake News Challenge stance detection taskcited by 26
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