# The Role of the Crowd in Countering Misinformation: A Case Study of the COVID-19 Infodemic

arXiv: https://arxiv.org/abs/2011.05773 · HTML: https://mlsanity.com/paper/2011.05773/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2020.

## Built on by (most cited first, 24 of 24)

- [DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection](https://mlsanity.com/paper/2402.10426/) (2024)
- [Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation](https://mlsanity.com/paper/2303.06433/) (2023)
- [Evidence-Driven Retrieval Augmented Response Generation for Online Misinformation](https://mlsanity.com/paper/2403.14952/) (2024)
- [Benchmarking the Generation of Fact Checking Explanations](https://mlsanity.com/paper/2308.15202/) (2023)
- [Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments](https://mlsanity.com/paper/2406.09815/) (2024)
- [Crowdsourced Fact-Checking at Twitter: How Does the Crowd Compare With Experts?](https://mlsanity.com/paper/2208.09214/) (2022)
- [How Robust are LLMs to In-Context Majority Label Bias?](https://mlsanity.com/paper/2312.16549/) (2023)
- [A Guide to Misinformation Detection Data and Evaluation](https://mlsanity.com/paper/2411.05060/) (2024)
- [Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of COVID-19 Infodemic](https://mlsanity.com/paper/2106.11702/) (2021)
- [Countering Misinformation via Emotional Response Generation](https://mlsanity.com/paper/2311.10587/) (2023)
- [COVID-19 Modeling: A Review](https://mlsanity.com/paper/2104.12556/) (2021)
- [Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluation](https://mlsanity.com/paper/2402.15988/) (2024)
- [Auditing and Robustifying COVID-19 Misinformation Datasets via Anticontent Sampling](https://mlsanity.com/paper/2310.07078/) (2023)
- [Speaking at the Right Level: Literacy-Controlled Counterspeech Generation with RAG-RL](https://mlsanity.com/paper/2509.01058/) (2025)
- [Hierarchical Query Classification in E-commerce Search](https://mlsanity.com/paper/2403.06021/) (2024)
- [GitSearch: Enhancing Community Notes Generation with Gap-Informed Targeted Search](https://mlsanity.com/paper/2602.08945/) (2026)
- [LTCR: Long-Text Chinese Rumor Detection Dataset](https://mlsanity.com/paper/2306.07201/) (2023)
- [Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else?](https://mlsanity.com/paper/2111.05139/) (2021)
- [A Weakly-Supervised Iterative Graph-Based Approach to Retrieve COVID-19 Misinformation Topics](https://mlsanity.com/paper/2205.09416/) (2022)
- [A Large-Scale Comparative Study of Accurate COVID-19 Information versus Misinformation](https://mlsanity.com/paper/2304.04811/) (2023)
- [Understanding the Humans Behind Online Misinformation: An Observational Study Through the Lens of the COVID-19 Pandemic](https://mlsanity.com/paper/2310.08483/) (2023)
- [Counterspeech for Mitigating the Influence of Media Bias: Comparing Human and LLM-Generated Responses](https://mlsanity.com/paper/2508.15855/) (2025)
- [Angry but Accurate: Detecting and Profiling the Counter-Misinformation Ecosystem on Twitter](https://mlsanity.com/paper/2607.02900/) (2026)
- [CoVer: Conflict-Aware Claim Verification](https://mlsanity.com/paper/2609.00508/) (2026)

## Abstract (arXiv)

Fact checking by professionals is viewed as a vital defense in the fight against misinformation.While fact checking is important and its impact has been significant, fact checks could have limited visibility and may not reach the intended audience, such as those deeply embedded in polarized communities. Concerned citizens (i.e., the crowd), who are users of the platforms where misinformation appears, can play a crucial role in disseminating fact-checking information and in countering the spread of misinformation. To explore if this is the case, we conduct a data-driven study of misinformation on the Twitter platform, focusing on tweets related to the COVID-19 pandemic, analyzing the spread of misinformation, professional fact checks, and the crowd response to popular misleading claims about COVID-19. In this work, we curate a dataset of false claims and statements that seek to challenge or refute them. We train a classifier to create a novel dataset of 155,468 COVID-19-related tweets, containing 33,237 false claims and 33,413 refuting arguments.Our findings show that professional fact-checking tweets have limited volume and reach. In contrast, we observe that the surge in misinformation tweets results in a quick response and a corresponding increase in tweets that refute such misinformation. More importantly, we find contrasting differences in the way the crowd refutes tweets, some tweets appear to be opinions, while others contain concrete evidence, such as a link to a reputed source. Our work provides insights into how misinformation is organically countered in social platforms by some of their users and the role they play in amplifying professional fact checks.These insights could lead to development of tools and mechanisms that can empower concerned citizens in combating misinformation. The code and data can be found in http://claws.cc.gatech.edu/covid_counter_misinformation.html.