Automated Evidence Collection for Fake News Detection

TL;DR

This paper introduces an automated method for fake news detection that gathers and summarizes supporting evidence from web articles, significantly improving detection accuracy over existing approaches.

Contribution

It presents a novel evidence collection and summarization technique that enhances fake news classification, outperforming state-of-the-art methods in accuracy.

Findings

Achieved an F1-score of 99.25 on the CONSTRAINT-2021 dataset.

Outperformed existing fake news detection methods.

Provided augmented dataset, code, and models for future research.

Abstract

Fake news, misinformation, and unverifiable facts on social media platforms propagate disharmony and affect society, especially when dealing with an epidemic like COVID-19. The task of Fake News Detection aims to tackle the effects of such misinformation by classifying news items as fake or real. In this paper, we propose a novel approach that improves over the current automatic fake news detection approaches by automatically gathering evidence for each claim. Our approach extracts supporting evidence from the web articles and then selects appropriate text to be treated as evidence sets. We use a pre-trained summarizer on these evidence sets and then use the extracted summary as supporting evidence to aid the classification task. Our experiments, using both machine learning and deep learning-based methods, help perform an extensive evaluation of our approach. The results show that our approach outperforms the state-of-the-art methods in fake news detection to achieve an F1-score of 99.25 over the dataset provided for the CONSTRAINT-2021 Shared Task. We also release the augmented dataset, our code and models for any further research.