Overview of Factify5WQA: Fact Verification through 5W Question-Answering

TL;DR

This paper presents the Factify5WQA shared task, which advances automated fake news detection by using aspect-based question answering with 5W questions to verify claims against supporting documents, showing significant performance improvements.

Contribution

It introduces a new dataset and methodology for fact verification using 5W question-answering, demonstrating improved accuracy over baseline models.

Findings

Best model achieved 69.56% accuracy

35% improvement over baseline performance

Effective use of BLEU score for answer comparison

Abstract

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.