Overview of Factify5WQA: Fact Verification through 5W Question-Answering
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Abstract (arXiv)
Researchers have found that fake news spreads much times faster than real news. This is a major problem, especially in today's world where social media is the key source of news for many among the younger population. Fact verification, thus, becomes an important task and many media sites contribute to the cause. Manual fact verification is a tedious task, given the volume of fake news online. The Factify5WQA shared task aims to increase research towards automated fake news detection by providing a dataset with an aspect-based question answering based fact verification method. Each claim and its supporting document is associated with 5W questions that help compare the two information sources. The objective performance measure in the task is done by comparing answers using BLEU score to measure the accuracy of the answers, followed by an accuracy measure of the classification. The task had submissions using custom training setup and pre-trained language-models among others. The best performing team posted an accuracy of 69.56%, which is a near 35% improvement over the baseline.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 17 of its 29 references that are in the map. First posted on arXiv 5 Oct 2024.
Builds on
17 of 29 references.
References · 17
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- 2021FaVIQ: FAct Verification from Information-seeking Questionscited by 36
- 2022CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweetscited by 29
5 more
- 2023FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answeringcited by 24
- 2023Factify 2: A Multimodal Fake News and Satire News Datasetcited by 23
- 2022Stanceosaurus: Classifying Stance Towards Multilingual Misinformationcited by 8
- 2023Findings of Factify 2: Multimodal Fake News Detectioncited by 7
- 2024Team Trifecta at Factify5WQA: Setting the Standard in Fact Verification with Fine-Tuningcited by 2
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Citing papers per year: 2025: 2.
2 papers.