A Heuristic-driven Ensemble Framework for COVID-19 Fake News Detection

TL;DR

This paper presents a heuristic-driven ensemble framework for COVID-19 fake news detection on social media, achieving state-of-the-art accuracy by combining pre-trained models with novel heuristics.

Contribution

The paper introduces a novel heuristic algorithm integrated into an ensemble model, significantly improving fake news detection accuracy for COVID-19 related tweets.

Findings

Achieved an F1-score of 0.9883, surpassing previous results.

Developed a heuristic algorithm based on username handles and link domains.

Placed 8th in the CONSTRAINT COVID19 Fake News Detection challenge.

Abstract

The significance of social media has increased manifold in the past few decades as it helps people from even the most remote corners of the world stay connected. With the COVID-19 pandemic raging, social media has become more relevant and widely used than ever before, and along with this, there has been a resurgence in the circulation of fake news and tweets that demand immediate attention. In this paper, we describe our Fake News Detection system that automatically identifies whether a tweet related to COVID-19 is "real" or "fake", as a part of CONSTRAINT COVID19 Fake News Detection in English challenge. We have used an ensemble model consisting of pre-trained models that has helped us achieve a joint 8th position on the leader board. We have achieved an F1-score of 0.9831 against a top score of 0.9869. Post completion of the competition, we have been able to drastically improve our system by incorporating a novel heuristic algorithm based on username handles and link domains in tweets fetching an F1-score of 0.9883 and achieving state-of-the art results on the given dataset.