Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News Detection

TL;DR

This paper introduces Cross-SEAN, a semi-supervised neural attention model leveraging external knowledge for COVID-19 fake news detection, achieving high accuracy and enabling real-time identification through a Chrome extension.

Contribution

It presents the first COVID-19 Twitter fake news dataset and a novel cross-stitch neural model that effectively uses unlabeled data and external knowledge for fake news detection.

Findings

Cross-SEAN achieves 0.95 F1 score on the COVID-19 Twitter fake news dataset.

Outperforms seven state-of-the-art fake news detection methods by 9%.

Develops a real-time Chrome extension for fake tweet detection.

Abstract

As the COVID-19 pandemic sweeps across the world, it has been accompanied by a tsunami of fake news and misinformation on social media. At the time when reliable information is vital for public health and safety, COVID-19 related fake news has been spreading even faster than the facts. During times such as the COVID-19 pandemic, fake news can not only cause intellectual confusion but can also place lives of people at risk. This calls for an immediate need to contain the spread of such misinformation on social media. We introduce CTF, the first COVID-19 Twitter fake news dataset with labeled genuine and fake tweets. Additionally, we propose Cross-SEAN, a cross-stitch based semi-supervised end-to-end neural attention model, which leverages the large amount of unlabelled data. Cross-SEAN partially generalises to emerging fake news as it learns from relevant external knowledge. We compare Cross-SEAN with seven state-of-the-art fake news detection methods. We observe that it achieves $0.95$ F1 Score on CTF, outperforming the best baseline by $9\%$. We also develop Chrome-SEAN, a Cross-SEAN based chrome extension for real-time detection of fake tweets.