Cross-lingual COVID-19 Fake News Detection
It addresses the problem of limited fact-checking resources in non-English languages for public health during the pandemic, though it is incremental as it applies known methods to a new domain.
The paper tackles detecting COVID-19 misinformation in Chinese using English fact-checked news, achieving effectiveness in cross-lingual settings and outperforming existing detectors.
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.