Cross-lingual COVID-19 Fake News Detection

TL;DR

This paper introduces CrossFake, a deep learning framework for detecting COVID-19 misinformation in Chinese by leveraging fact-checked English news, demonstrating effectiveness in cross-lingual fake news detection.

Contribution

It is the first to address COVID-19 fake news detection in a low-resource language using high-resource language fact-checking data, with a new dataset and a novel cross-lingual model.

Findings

CrossFake outperforms monolingual and other cross-lingual detectors.

The dataset is publicly available for further research.

Empirical results show high effectiveness in cross-lingual settings.

Abstract

The COVID-19 pandemic poses a great threat to global public health. Meanwhile, there is massive misinformation associated with the pandemic which advocates unfounded or unscientific claims. Even major social media and news outlets have made an extra effort in debunking COVID-19 misinformation, most of the fact-checking information is in English, whereas some unmoderated COVID-19 misinformation is still circulating in other languages, threatening the health of less-informed people in immigrant communities and developing countries. In this paper, we make the first attempt to detect COVID-19 misinformation in a low-resource language (Chinese) only using the fact-checked news in a high-resource language (English). We start by curating a Chinese real&fake news dataset according to existing fact-checking information. Then, we propose a deep learning framework named CrossFake to jointly encode the cross-lingual news body texts and capture the news content as much as possible. Empirical results on our dataset demonstrate the effectiveness of CrossFake under the cross-lingual setting and it also outperforms several monolingual and cross-lingual fake news detectors. The dataset is available at https://github.com/YingtongDou/CrossFake.