Cross-lingual COVID-19 Fake News Detection

Jiangshu Du, Yingtong Dou, Congying Xia, Limeng Cui, Jing Ma, Philip S. Yu

Research output: Chapter in book/report/conference proceedingConference proceeding

22 Citations (Scopus)


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 CorssFake under the cross-lingual setting and it also outperforms several monolingual and cross-lingual fake news detectors. The dataset is available at
Original languageEnglish
Title of host publication2021 International Conference on Data Mining Workshops (ICDMW)
Number of pages4
ISBN (Electronic)9781665424271
ISBN (Print)9781665424288
Publication statusPublished - Dec 2021
Event21st IEEE International Conference on Data Mining Workshops, ICDMW 2021 - Auckland, New Zealand
Duration: 7 Dec 202110 Dec 2021

Publication series

NameInternational Conference on Data Mining Workshops (ICDMW)
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259


Conference21st IEEE International Conference on Data Mining Workshops, ICDMW 2021
Country/TerritoryNew Zealand
Internet address

User-Defined Keywords

  • fake news
  • COVID-19
  • cross-lingual
  • dataset


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