mlsanityData to 7 Oct 2026

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

arXiv 2011.05773 ↗First posted 11 Nov 2020cs.SI cs.CL cs.CYcited by 24

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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.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Nov 2020.

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Citing papers per year: 2021: 3; 2022: 2; 2023: 8; 2024: 6; 2025: 2; 2026: 3.

24 papers.

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  1. 2024DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detectioncited by 41
  2. 2023Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformationcited by 39
  3. 2024Evidence-Driven Retrieval Augmented Response Generation for Online Misinformationcited by 32
  4. 2023Benchmarking the Generation of Fact Checking Explanationscited by 25
  5. 2024Retrieval Augmented Fact Verification by Synthesizing Contrastive Argumentscited by 25
  6. 2022Crowdsourced Fact-Checking at Twitter: How Does the Crowd Compare With Experts?cited by 16
  7. 2023How Robust are LLMs to In-Context Majority Label Bias?cited by 13
  8. 2024A Guide to Misinformation Detection Data and Evaluationcited by 10
  9. 2021Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of COVID-19 Infodemiccited by 9
  10. 2023Countering Misinformation via Emotional Response Generationcited by 9
  11. 2021COVID-19 Modeling: A Reviewcited by 7
  12. 2024Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluationcited by 6
12 more
  1. 2023Auditing and Robustifying COVID-19 Misinformation Datasets via Anticontent Samplingcited by 3
  2. 2025Speaking at the Right Level: Literacy-Controlled Counterspeech Generation with RAG-RLcited by 3
  3. 2024Hierarchical Query Classification in E-commerce Searchcited by 2
  4. 2026GitSearch: Enhancing Community Notes Generation with Gap-Informed Targeted Searchcited by 2
  5. 2023LTCR: Long-Text Chinese Rumor Detection Datasetcited by 1
  6. 2021Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else?cited by 0
  7. 2022A Weakly-Supervised Iterative Graph-Based Approach to Retrieve COVID-19 Misinformation Topicscited by 0
  8. 2023A Large-Scale Comparative Study of Accurate COVID-19 Information versus Misinformationcited by 0
  9. 2023Understanding the Humans Behind Online Misinformation: An Observational Study Through the Lens of the COVID-19 Pandemiccited by 0
  10. 2025Counterspeech for Mitigating the Influence of Media Bias: Comparing Human and LLM-Generated Responsescited by 0
  11. 2026Angry but Accurate: Detecting and Profiling the Counter-Misinformation Ecosystem on Twittercited by 0
  12. 2026CoVer: Conflict-Aware Claim Verificationcited by 0