# MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation

arXiv: https://arxiv.org/abs/2011.04088 · HTML: https://mlsanity.com/paper/2011.04088/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2020.

## Built on by (most cited first, 30 of 37)

- [Memory-Guided Multi-View Multi-Domain Fake News Detection](https://mlsanity.com/paper/2206.12808/) (2022)
- [Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation Generation](https://mlsanity.com/paper/2309.08793/) (2023)
- [Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer](https://mlsanity.com/paper/2209.08902/) (2022)
- [Can LLMs Improve Multimodal Fact-Checking by Asking Relevant Questions?](https://mlsanity.com/paper/2410.04616/) (2024)
- [Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities](https://mlsanity.com/paper/2203.13883/) (2022)
- [Transformer based Automatic COVID-19 Fake News Detection System](https://mlsanity.com/paper/2101.00180/) (2021)
- [MMCoVaR: Multimodal COVID-19 Vaccine Focused Data Repository for Fake News Detection and a Baseline Architecture for Classification](https://mlsanity.com/paper/2109.06416/) (2021)
- [A Survey on Echo Chambers on Social Media: Description, Detection and Mitigation](https://mlsanity.com/paper/2112.05084/) (2021)
- [Dataset of Fake News Detection and Fact Verification: A Survey](https://mlsanity.com/paper/2111.03299/) (2021)
- [A Guide to Misinformation Detection Data and Evaluation](https://mlsanity.com/paper/2411.05060/) (2024)
- ["This is Fake! Shared it by Mistake": Assessing the Intent of Fake News Spreaders](https://mlsanity.com/paper/2202.04752/) (2022)
- [Heterogeneous Subgraph Transformer for Fake News Detection](https://mlsanity.com/paper/2404.13192/) (2024)
- [Beyond Translation: LLM-Based Data Generation for Multilingual Fact-Checking](https://mlsanity.com/paper/2502.15419/) (2025)
- [PHEMEPlus: Enriching Social Media Rumour Verification with External Evidence](https://mlsanity.com/paper/2207.13970/) (2022)
- [FineFake: A Knowledge-Enriched Dataset for Fine-Grained Multi-Domain Fake News Detection](https://mlsanity.com/paper/2404.01336/) (2024)
- [The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection](https://mlsanity.com/paper/2601.15316/) (2026)
- [JailNewsBench: Multi-Lingual and Regional Benchmark for Fake News Generation under Jailbreak Attacks](https://mlsanity.com/paper/2603.01291/) (2026)
- [Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims](https://mlsanity.com/paper/2205.02596/) (2022)
- [Breaking Language Barriers with MMTweets: Advancing Cross-Lingual Debunked Narrative Retrieval for Fact-Checking](https://mlsanity.com/paper/2308.05680/) (2023)
- [Fake News Detection: Comparative Evaluation of BERT-like Models and Large Language Models with Generative AI-Annotated Data](https://mlsanity.com/paper/2412.14276/) (2024)
- [Towards Detecting Harmful Agendas in News Articles](https://mlsanity.com/paper/2302.00102/) (2023)
- [PANACEA: An Automated Misinformation Detection System on COVID-19](https://mlsanity.com/paper/2303.01241/) (2023)
- [When Scale Meets Diversity: Evaluating Language Models on Fine-Grained Multilingual Claim Verification](https://mlsanity.com/paper/2507.20700/) (2025)
- [Linguistic-style-aware Neural Networks for Fake News Detection](https://mlsanity.com/paper/2301.02792/) (2023)
- [Dual-Teacher De-biasing Distillation Framework for Multi-domain Fake News Detection](https://mlsanity.com/paper/2312.01006/) (2023)
- [BLUFF: Benchmarking the Detection of False and Synthetic Content across 58 Low-Resource Languages](https://mlsanity.com/paper/2603.00634/) (2026)
- [Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectives](https://mlsanity.com/paper/2107.09768/) (2021)
- [Addressing contingency in algorithmic (mis)information classification: Toward a responsible machine learning agenda](https://mlsanity.com/paper/2210.09014/) (2022)
- [A Roadmap for Multilingual, Multimodal Domain Independent Deception Detection](https://mlsanity.com/paper/2405.03920/) (2024)
- [From Generation to Detection: A Multimodal Multi-Task Dataset for Benchmarking Health Misinformation](https://mlsanity.com/paper/2505.18685/) (2025)

## Abstract (arXiv)

The COVID-19 epidemic is considered as the global health crisis of the whole society and the greatest challenge mankind faced since World War Two. Unfortunately, the fake news about COVID-19 is spreading as fast as the virus itself. The incorrect health measurements, anxiety, and hate speeches will have bad consequences on people's physical health, as well as their mental health in the whole world. To help better combat the COVID-19 fake news, we propose a new fake news detection dataset MM-COVID(Multilingual and Multidimensional COVID-19 Fake News Data Repository). This dataset provides the multilingual fake news and the relevant social context. We collect 3981 pieces of fake news content and 7192 trustworthy information from English, Spanish, Portuguese, Hindi, French and Italian, 6 different languages. We present a detailed and exploratory analysis of MM-COVID from different perspectives and demonstrate the utility of MM-COVID in several potential applications of COVID-19 fake news study on multilingual and social media.