# Cross-lingual COVID-19 Fake News Detection

arXiv: https://arxiv.org/abs/2110.06495 · HTML: https://mlsanity.com/paper/2110.06495/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 13 Oct 2021.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Multilingual Universal Sentence Encoder for Semantic Retrieval](https://mlsanity.com/paper/1907.04307/) (2019)
- [CSI: A Hybrid Deep Model for Fake News Detection](https://mlsanity.com/paper/1703.06959/) (2017)
- [FANG: Leveraging Social Context for Fake News Detection Using Graph Representation](https://mlsanity.com/paper/2008.07939/) (2020)
- [A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1510.03820/) (2015)
- [CoAID: COVID-19 Healthcare Misinformation Dataset](https://mlsanity.com/paper/2006.00885/) (2020)
- [FakeCovid -- A Multilingual Cross-domain Fact Check News Dataset for COVID-19](https://mlsanity.com/paper/2006.11343/) (2020)
- [ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research](https://mlsanity.com/paper/2006.05557/) (2020)
- [Cross-lingual Zero- and Few-shot Hate Speech Detection Utilising Frozen Transformer Language Models and AXEL](https://mlsanity.com/paper/2004.13850/) (2020)
- [CHECKED: Chinese COVID-19 Fake News Dataset](https://mlsanity.com/paper/2010.09029/) (2020)
- [Detecting Social Media Manipulation in Low-Resource Languages](https://mlsanity.com/paper/2011.05367/) (2020)

## Built on by (most cited first, 7 of 7)

- [Zero-Shot Rumor Detection with Propagation Structure via Prompt Learning](https://mlsanity.com/paper/2212.01117/) (2022)
- [Combating Online Misinformation Videos: Characterization, Detection, and Future Directions](https://mlsanity.com/paper/2302.03242/) (2023)
- [FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection](https://mlsanity.com/paper/2303.07113/) (2023)
- [Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish](https://mlsanity.com/paper/2403.00411/) (2024)
- [UrduFactCheck: An Agentic Fact-Checking Framework for Urdu with Evidence Boosting and Benchmarking](https://mlsanity.com/paper/2505.15063/) (2025)
- [Tackling Fake News in Bengali: Unraveling the Impact of Summarization vs. Augmentation on Pre-trained Language Models](https://mlsanity.com/paper/2307.06979/) (2023)
- [CANDY: Benchmarking LLMs' Limitations and Assistive Potential in Chinese Misinformation Fact-Checking](https://mlsanity.com/paper/2509.03957/) (2025)

## Abstract (arXiv)

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.