# ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research

arXiv: https://arxiv.org/abs/2006.05557 · HTML: https://mlsanity.com/paper/2006.05557/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2020.

## Built on by (most cited first, 30 of 44)

- [Memory-Guided Multi-View Multi-Domain Fake News Detection](https://mlsanity.com/paper/2206.12808/) (2022)
- [Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer](https://mlsanity.com/paper/2209.08902/) (2022)
- [LLM-Generated Fake News Induces Truth Decay in News Ecosystem: A Case Study on Neural News Recommendation](https://mlsanity.com/paper/2504.20013/) (2025)
- [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)
- [Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detection](https://mlsanity.com/paper/2109.12986/) (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)
- [AI in the Gray: Exploring Moderation Policies in Dialogic Large Language Models vs. Human Answers in Controversial Topics](https://mlsanity.com/paper/2308.14608/) (2023)
- [Dataset of Fake News Detection and Fact Verification: A Survey](https://mlsanity.com/paper/2111.03299/) (2021)
- [Each Fake News is Fake in its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection](https://mlsanity.com/paper/2412.14686/) (2024)
- [Testing the Generalization of Neural Language Models for COVID-19 Misinformation Detection](https://mlsanity.com/paper/2111.07819/) (2021)
- [Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of COVID-19 Infodemic](https://mlsanity.com/paper/2106.11702/) (2021)
- ["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)
- [VLDBench Evaluating Multimodal Disinformation with Regulatory Alignment](https://mlsanity.com/paper/2502.11361/) (2025)
- [Cross-lingual COVID-19 Fake News Detection](https://mlsanity.com/paper/2110.06495/) (2021)
- [PHEMEPlus: Enriching Social Media Rumour Verification with External Evidence](https://mlsanity.com/paper/2207.13970/) (2022)
- [A Second Pandemic? Analysis of Fake News About COVID-19 Vaccines in Qatar](https://mlsanity.com/paper/2109.11372/) (2021)
- [Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims](https://mlsanity.com/paper/2205.02596/) (2022)
- [Construction of Large-Scale Misinformation Labeled Datasets from Social Media Discourse using Label Refinement](https://mlsanity.com/paper/2202.12413/) (2022)
- [Applying Automatic Text Summarization for Fake News Detection](https://mlsanity.com/paper/2204.01841/) (2022)
- [The COVMis-Stance dataset: Stance Detection on Twitter for COVID-19 Misinformation](https://mlsanity.com/paper/2204.02000/) (2022)
- [DANES: Deep Neural Network Ensemble Architecture for Social and Textual Context-aware Fake News Detection](https://mlsanity.com/paper/2302.01756/) (2023)
- [ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination](https://mlsanity.com/paper/2201.06496/) (2022)
- [Unsupervised Domain-agnostic Fake News Detection using Multi-modal Weak Signals](https://mlsanity.com/paper/2305.11349/) (2023)
- [AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News and Hate Speech Detection Dataset](https://mlsanity.com/paper/2105.03143/) (2021)
- [Like Article, Like Audience: Enforcing Multimodal Correlations for Disinformation Detection](https://mlsanity.com/paper/2108.13892/) (2021)
- [Supervised Contrastive Learning for Multimodal Unreliable News Detection in COVID-19 Pandemic](https://mlsanity.com/paper/2109.01850/) (2021)
- [QCRI's COVID-19 Disinformation Detector: A System to Fight the COVID-19 Infodemic in Social Media](https://mlsanity.com/paper/2204.03506/) (2022)
- [Multiverse: Multilingual Evidence for Fake News Detection](https://mlsanity.com/paper/2211.14279/) (2022)

## Abstract (arXiv)

First identified in Wuhan, China, in December 2019, the outbreak of COVID-19 has been declared as a global emergency in January, and a pandemic in March 2020 by the World Health Organization (WHO). Along with this pandemic, we are also experiencing an "infodemic" of information with low credibility such as fake news and conspiracies. In this work, we present ReCOVery, a repository designed and constructed to facilitate research on combating such information regarding COVID-19. We first broadly search and investigate ~2,000 news publishers, from which 60 are identified with extreme [high or low] levels of credibility. By inheriting the credibility of the media on which they were published, a total of 2,029 news articles on coronavirus, published from January to May 2020, are collected in the repository, along with 140,820 tweets that reveal how these news articles have spread on the Twitter social network. The repository provides multimodal information of news articles on coronavirus, including textual, visual, temporal, and network information. The way that news credibility is obtained allows a trade-off between dataset scalability and label accuracy. Extensive experiments are conducted to present data statistics and distributions, as well as to provide baseline performances for predicting news credibility so that future methods can be compared. Our repository is available at http://coronavirus-fakenews.com.