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.