---
title: "COVIDLies — paper by Hossain et al. (2020)"
description: "They release COVIDLies, which pairs 86 misconceptions with tweets in 6761 pairs labeled by UCI School of Medicine… 52 references, cited by 62 papers here."
authors: ["Tamanna Hossain", "Robert L. Logan IV", "Arjuna Ugarte", "Yoshitomo Matsubara", "Sean Young", "Sameer Singh"]
year: 2020
venue: "Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020"
source: "https://aclanthology.org/2020.nlpcovid19-2.11/"
doi: "10.18653/v1/2020.nlpcovid19-2.11"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/hossain-2020-covidlies-detecting-covid-19-misinformation/"
---
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# COVIDLies: Detecting COVID-19 Misinformation on Social Media

**Authors:** Tamanna Hossain, [Robert L. Logan IV](https://md.research.elamur.ai/free/en/authors/robert-l-logan-iv/), Arjuna Ugarte, Yoshitomo Matsubara, Sean Young and [Sameer Singh](https://md.research.elamur.ai/free/en/authors/sameer-singh/) · **Year:** 2020 · **Venue:** [Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020](https://md.research.elamur.ai/free/en/venues/nlpcovid19/) ([Workshop on NLP for COVID-19 at EMNLP 2020](https://md.research.elamur.ai/free/en/venues/nlpcovid19/2020/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.nlpcovid19-2.11/) · [PDF](https://aclanthology.org/2020.nlpcovid19-2.11.pdf) · [DOI 10.18653/v1/2020.nlpcovid19-2.11](https://doi.org/10.18653/v1/2020.nlpcovid19-2.11) · [OpenAlex](https://openalex.org/W3097301690)

**Citations:** 208 citations (OpenAlex, 2026-10-14) · [62](https://md.research.elamur.ai/free/en/papers/hossain-2020-covidlies-detecting-covid-19-misinformation/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper addresses automatic detection of COVID-19 misinformation on Twitter. Older misinformation benchmarks fit poorly here because pandemic vocabulary was new and the facts kept shifting. The authors split the problem into two steps. First, retrieve known misconceptions relevant to a tweet. Second, decide the tweet's stance toward each retrieved misconception: Agree, Disagree, or No Stance.

They release COVIDLies, which pairs 86 misconceptions with tweets in 6761 pairs labeled by UCI School of Medicine researchers. The misconceptions come from a Wikipedia article on pandemic misinformation, rewritten as short positive claims. Candidate tweets for annotation were chosen with BERTScore.

For benchmarks, retrieval is treated as semantic similarity and stance detection as NLI (Agree→Entailment, Disagree→Contradiction, No Stance→Neutral). Off-the-shelf models perform weakly. Pretraining language models on COVID-19 tweets (domain adaptation) helps substantially, but leaves much room for improvement.

The resource matters because it shows how far current NLP tools are from being deployable against a fast-moving "infodemic."

## Key points
- COVIDLies has 86 curated misconceptions and 6761 expert-labeled tweet–misconception pairs. The tweets are English posts from March–April 2020, taken from the Chen et al. (2020) COVID-19 Twitter collection.
- For each misconception, the 100 tweets scored most similar by BERTScore were sent to annotators. The resulting labels skew heavily toward No Stance, with more Agree than Disagree pairs.
- Inter-annotator reliability was measured on 200 pairs labeled by four researchers. Raw agreement was 79% and Fleiss Kappa was 0.69. Disagreements were discussed and resolved before the rest of the data was labeled.
- Baseline results are 38.7 Hits@1 for retrieval and 32.5 macro F1 for stance detection. With domain adaptation, these rise to 61.3 Hits@1 and 50.2 macro F1.
- One of the systems is a cascade. BERTScore (DA) first separates relevant pairs from No Stance pairs, and SBERT (DA) trained on MultiNLI then classifies stance only for the relevant pairs.

## Interesting ideas
- **Misinformation detection as retrieval plus stance.** The system compares posts against a maintained list of known false claims instead of judging truth directly. This makes the system easy to update as new rumors appear, and the same design could suit any domain where false claims recur, such as trade or health rumors.
- **Recasting stance as NLI.** Mapping stance labels onto NLI classes lets existing NLI datasets supply supervision when no in-domain training data exists. This is useful for low-resource languages or new topics.
- **Rewriting claims as short positive statements.** Normalizing each misconception into one affirmative sentence makes similarity matching and entailment more consistent across claims.
- **Cheap domain adaptation.** Continuing language-model pretraining on unlabeled in-domain text, here COVID tweets, gave large gains on both subtasks. This is attractive wherever unlabeled text is plentiful but labels are scarce.
- **Reporting the bias of the sampler.** The authors note that BERTScore-based candidate selection may explain the dominance of No Stance and the surplus of Agree labels. Reporting the side effects of a sampling tool like this is good practice for anyone building datasets with retrieval-assisted annotation.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of California, Irvine](https://md.research.elamur.ai/free/en/universities/university-of-california-irvine/) — University, Irvine, United States · 29 works · 36,049 citations

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## Cite

Cite as: Tamanna Hossain, Robert L. Logan IV, Arjuna Ugarte, Yoshitomo Matsubara, Sean Young and Sameer Singh. 2020. COVIDLies: Detecting COVID-19 Misinformation on Social Media. Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{hossain2020covidlies,
  title = {{COVIDLies: Detecting COVID-19 Misinformation on Social Media}},
  author = {Tamanna Hossain, Robert L. Logan IV, Arjuna Ugarte, Yoshitomo Matsubara, Sean Young and Sameer Singh},
  booktitle = {Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020},
  year = {2020},
  doi = {10.18653/v1/2020.nlpcovid19-2.11},
  url = {https://doi.org/10.18653/v1/2020.nlpcovid19-2.11}
}
```

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*Updated 24 October 2026*