# Fighting an Infodemic: COVID-19 Fake News Dataset

arXiv: https://arxiv.org/abs/2011.03327 · HTML: https://mlsanity.com/paper/2011.03327/

## Key facts

- Cited by 82 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 6 Nov 2020.

## Built on by (most cited first, 30 of 82)

- [X-FACT: A New Benchmark Dataset for Multilingual Fact Checking](https://mlsanity.com/paper/2106.09248/) (2021)
- [A Survey on Multimodal Disinformation Detection](https://mlsanity.com/paper/2103.12541/) (2021)
- [Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformation](https://mlsanity.com/paper/2210.13865/) (2022)
- [Evidence-Driven Retrieval Augmented Response Generation for Online Misinformation](https://mlsanity.com/paper/2403.14952/) (2024)
- [COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learning](https://mlsanity.com/paper/2101.06278/) (2021)
- [Factify 2: A Multimodal Fake News and Satire News Dataset](https://mlsanity.com/paper/2304.03897/) (2023)
- [Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs](https://mlsanity.com/paper/2408.12060/) (2024)
- [Emotion Detection for Misinformation: A Review](https://mlsanity.com/paper/2311.00671/) (2023)
- [Combat COVID-19 Infodemic Using Explainable Natural Language Processing Models](https://mlsanity.com/paper/2103.00747/) (2021)
- [Evaluating Deep Learning Approaches for Covid19 Fake News Detection](https://mlsanity.com/paper/2101.04012/) (2021)
- [Twitter-COMMs: Detecting Climate, COVID, and Military Multimodal Misinformation](https://mlsanity.com/paper/2112.08594/) (2021)
- [FakeGPT: Fake News Generation, Explanation and Detection of Large Language Models](https://mlsanity.com/paper/2310.05046/) (2023)
- [Transformer-based Language Model Fine-tuning Methods for COVID-19 Fake News Detection](https://mlsanity.com/paper/2101.05509/) (2021)
- [Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News Detection](https://mlsanity.com/paper/2102.08924/) (2021)
- [Contrastive Domain Adaptation for Early Misinformation Detection: A Case Study on COVID-19](https://mlsanity.com/paper/2208.09578/) (2022)
- [Transformer based Automatic COVID-19 Fake News Detection System](https://mlsanity.com/paper/2101.00180/) (2021)
- [Knowledge Graph informed Fake News Classification via Heterogeneous Representation Ensembles](https://mlsanity.com/paper/2110.10457/) (2021)
- [Multimodal Fake News Detection](https://mlsanity.com/paper/2112.04831/) (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)
- [Continuous Detection, Rapidly React: Unseen Rumors Detection based on Continual Prompt-Tuning](https://mlsanity.com/paper/2203.11720/) (2022)
- [Exploring Text-transformers in AAAI 2021 Shared Task: COVID-19 Fake News Detection in English](https://mlsanity.com/paper/2101.02359/) (2021)
- [Model Generalization on COVID-19 Fake News Detection](https://mlsanity.com/paper/2101.03841/) (2021)
- [Dataset of Fake News Detection and Fact Verification: A Survey](https://mlsanity.com/paper/2111.03299/) (2021)
- [A Heuristic-driven Ensemble Framework for COVID-19 Fake News Detection](https://mlsanity.com/paper/2101.03545/) (2021)
- [TELLER: A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection](https://mlsanity.com/paper/2402.07776/) (2024)
- [Evaluation of Fake News Detection with Knowledge-Enhanced Language Models](https://mlsanity.com/paper/2204.00458/) (2022)
- [MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning](https://mlsanity.com/paper/2305.12692/) (2023)
- [Findings of Factify 2: Multimodal Fake News Detection](https://mlsanity.com/paper/2307.10475/) (2023)
- [Combating Hostility: Covid-19 Fake News and Hostile Post Detection in Social Media](https://mlsanity.com/paper/2101.03291/) (2021)
- [Fake News Detection System using XLNet model with Topic Distributions: CONSTRAINT@AAAI2021 Shared Task](https://mlsanity.com/paper/2101.11425/) (2021)

## Abstract (arXiv)

Along with COVID-19 pandemic we are also fighting an `infodemic'. Fake news and rumors are rampant on social media. Believing in rumors can cause significant harm. This is further exacerbated at the time of a pandemic. To tackle this, we curate and release a manually annotated dataset of 10,700 social media posts and articles of real and fake news on COVID-19. We benchmark the annotated dataset with four machine learning baselines - Decision Tree, Logistic Regression, Gradient Boost, and Support Vector Machine (SVM). We obtain the best performance of 93.46% F1-score with SVM. The data and code is available at: https://github.com/parthpatwa/covid19-fake-news-dectection