Elamur Research⌕
☰
home / papers / 2020 / COVIDLies: Detecting COVID-19 Misinformation on Social Media

COVIDLies: Detecting COVID-19 Misinformation on Social Media

Paper · 2020 · Tamanna Hossain, Robert L. Logan IV, Arjuna Ugarte et al.

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

  • I1. 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.
  • I2. 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.
  • I3. Rewriting claims as short positive statements. Normalizing each misconception into one affirmative sentence makes similarity matching and entailment more consistent across claims.
  • I4. 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.
  • I5. 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, IrvineIrvine · United States29 works · 36,049 citations

📚 Shelves 7 Computer Science · 9,086 works Artificial Intelligence · 8,889 works Arts and Humanities · 9,296 works Language and Linguistics · 9,206 works Social Sciences · 744 works Information Systems · 523 works Sociology and Political Science · 221 works

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. DOI 10.18653/v1/2020.nlpcovid19-2.11

@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}
}

References

52 references, 8 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. BBC. 2020. ’Hundreds dead’ because of Covid-19 misinformation. https://www.bbc.com/news/world-53755067 [Accessed on June 30, 2020]. ↗
  2. Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. A large annotated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics. find
  3. J Scott Brennen, Felix M Simon, Philip N Howard, and Rasmus Kleis Nielsen. 2020. Types, sources, and claims of COVID-19 misinformation. Reuters Institute. find
  4. Carlos Castillo, Marcelo Mendoza, and Barbara Poblete. 2011. Information credibility on twitter. In Proceedings of the 20th international conference on World wide web, pages 675–684. find
  5. Emily Chen, Kristina Lerman, and Emilio Ferrara. 2020. Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set. JMIR Public Health and Surveillance, 6(2):e19273. find
  6. Giovanni Luca Ciampaglia, Prashant Shiralkar, Luis M Rocha, Johan Bollen, Filippo Menczer, and Alessandro Flammini. 2015. Computational fact checking from knowledge networks. PloS one, 10(6):e0128193. find
  7. CNN. 2020. Covid-19 misinformation is spreading in 25 different languages, leading to deaths and injuries, study finds. https://www.cnn.com/2020/08/10/health/covid-misinformation-spread-study-wellness/index.html [Accessed on June 30, 2020]. ↗
  8. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. our card
  9. Arkin R Dharawat, Ismini Lourentzou, Alex Morales, and ChengXiang Zhai. 2020. Drink bleach or do what now? Covid-HeRA: A dataset for risk-informed health decision making in the presence of COVID19 misinformation. https://openreview.net/forum?id=PmY1SNmJlEC [Accessed on July 1, 2020]. ↗
  10. William Ferreira and Andreas Vlachos. 2016. Emergent: a novel data-set for stance classification. In Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: Human language technologies, pages 1163–1168. our card
  11. Genevieve Gorrell, Kalina Bontcheva, Leon Derczynski, Elena Kochkina, Maria Liakata, and Arkaitz Zubiaga. 2018. find
  12. Rumoureval 2019: Determining rumour veracity and support for rumours. arXiv preprint arXiv:1809.06683. — [arXiv:1809.06683](https://arxiv.org/abs/1809.06683) find
  13. Suchin Gururangan, Ana Marasovi, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A. Smith. 2020. Don’t stop pretraining: Adapt language models to domains and tasks. In Proceedings of ACL. find
  14. Hamid Karimi, Proteek Roy, Sari Saba-Sadiya, and Jiliang Tang. 2018. Multi-source multi-class fake news detection. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1546–1557. find
  15. Ramez Kouzy, Joseph Abi Jaoude, Afif Kraitem, Molly B El Alam, Basil Karam, Elio Adib, Jabra Zarka, Cindy Traboulsi, Elie W Akl, and Khalil Baddour. 2020. Coronavirus goes viral: quantifying the COVID-19 misinformation epidemic on Twitter. Cureus, 12(3). find
  16. Srijan Kumar, Robert West, and Jure Leskovec. 2016. Disinformation on the Web: Impact, Characteristics, and Detection of Wikipedia Hoaxes. In Proceedings of the 25th international conference on World Wide Web, pages 591–602. find
  17. Yaliang Li, Qi Li, Jing Gao, Lu Su, Bo Zhao, Wei Fan, and Jiawei Han. 2015. On the discovery of evolving truth. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 675–684. find
  18. Yunyao Li, Tyrone Grandison, Patricia Silveyra, Ali Douraghy, Xinyu Guan, Thomas Kieselbach, Chengkai Li, and Haiqi Zhang. 2020. Jennifer for COVID-19: An NLP-Powered Chatbot Built for the People and by the People to Combat Misinformation. find
  19. ACL 2020 Workshop NLP-COVID. find
  20. Cristian Lumezanu, Nick Feamster, and Hans Klein. 2012. \# bias: Measuring the tweeting behavior of propagandists. In Sixth International AAAI Conference on Weblogs and Social Media. find
  21. Liz McQuillan, Erin McAweeney, Alicia Bargar, and Alex Ruch. 2020. Cultural convergence: Insights into the behavior of misinformation networks on twitter. arXiv preprint arXiv:2007.03443. — [arXiv:2007.03443](https://arxiv.org/abs/2007.03443) find
  22. Marcelo Mendoza, Barbara Poblete, and Carlos Castillo. 2010. Twitter under crisis: Can we trust what we RT? In Proceedings of the first workshop on social media analytics, pages 71–79. find
  23. Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry. 2016. SemEval-2016 Task 6: Detecting Stance in Tweets. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pages 31– 41. our card
  24. Martin Müller, Marcel Salathé, and Per E Kummervold. 2020. COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter. arXiv preprint arXiv:2005.07503. find
  25. New York Times. 2020. Coronavirus doctors battle another scourge: Misinformation. https://www.nytimes.com/2020/08/17/technology/ coronavirus-disinformation-doctors.html [Accessed on June 30, 2020]. ↗
  26. Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global Vectors for Word Representation. In Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543. our card
  27. Gordon Pennycook, Jonathon McPhetres, Yunhao Zhang, and David Rand. 2020. Fighting COVID-19 misinformation on social media: Experimental evidence for a scalable accuracy nudge intervention. PsyArXiv Preprints, 10. find
  28. Poynter. 2020. The coronavirusfacts/datoscoronavirus alliance database. https://www.poynter.org/ifcn-covid-19-misinformation/ [Accessed on June 30, 2020]. ↗
  29. Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks. arXiv preprint arXiv:1908.10084. our card
  30. Juan Carlos Medina Serrano, Orestis Papakyriakopoulos, and Simon Hegelich. 2020. NLP-based Feature Extraction for the Detection of COVID-19 Misinformation Videos on YouTube. find
  31. ACL 2020 Workshop NLP-COVID. find
  32. Shivade. 2019. MedNLI - A Natural Language Inference Dataset For The Clinical Domain. doi.org/10.13026/C2RS98. — [doi:10.13026/c2rs98](https://doi.org/10.13026/c2rs98) find
  33. Kai Shu, Deepak Mahudeswaran, Suhang Wang, Dongwon Lee, and Huan Liu. 2020. FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media. Big Data, 8(3):171–188. find
  34. Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. 2017. Fake news detection on social media: A data mining perspective. ACM SIGKDD explorations newsletter, 19(1):22–36. find
  35. Kai Shu, Suhang Wang, and Huan Liu. 2019. Beyond news contents: The role of social context for fake news detection. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pages 312–320. find
  36. Eugenio Tacchini, Gabriele Ballarin, Marco L Della Vedova, Stefano Moret, and Luca de Alfaro. 2017. Some like it hoax: Automated fake news detection in social networks. arXiv preprint arXiv:1704.07506. — [arXiv:1704.07506](https://arxiv.org/abs/1704.07506) find
  37. TechCrunch. 2020. Facebook, reddit, google, linkedin, microsoft, twitter and youtube issue joint statement on misinformation. TechCrunch. https://techcrunch.com/2020/03/16/facebook-reddit-google-linkedin-microsoft-twitter-and-youtube-issue-joint-statement-on-misinformation/ [Accessed on June 30, 20… ↗
  38. James Thorne, Mingjie Chen, Giorgos Myrianthous, Jiashu Pu, Xiaoxuan Wang, and Andreas Vlachos. 2017. Fake news stance detection using stacked ensemble of classifiers. In Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism, pages 80–83, Copenhagen, Denmark. Associati… find
  39. James Thorne and Andreas Vlachos. 2018. Automated fact checking: Task formulations, methods and future directions. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3346–3359, Santa Fe, New Mexico, USA. Association for Computational Linguistics. our card
  40. James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, and Arpit Mittal. 2018. The fact extraction and verification (fever) shared task. arXiv preprint arXiv:1811.10971. our card
  41. James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, and Arpit Mittal. 2019. The FEVER2.0 Shared Task. In Second Workshop on Fact Extraction and VERification (FEVER), pages 1–6. find
  42. Sebastian Tschiatschek, Adish Singla, Manuel Gomez Rodriguez, Arpit Merchant, and Andreas Krause. 2018. Fake news detection in social networks via crowd signals. In Companion Proceedings of the The Web Conference 2018, pages 517–524. find
  43. Svitlana Volkova, Kyle Shaffer, Jin Yea Jang, and Nathan Hodas. 2017. Separating facts from fiction: Linguistic models to classify suspicious and trusted news posts on twitter. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages… find
  44. G VolppKevin et al. 2020. Asked and answered: Building a chatbot to address covid-19-related concerns. NEJM Catalyst Innovations in Care Delivery. find
  45. William Yang Wang. 2017. ”Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection. arXiv preprint arXiv:1705.00648. our card
  46. Wei Wei and Xiaojun Wan. 2017. Learning to identify ambiguous and misleading news headlines. arXiv preprint arXiv:1705.06031. — [arXiv:1705.06031](https://arxiv.org/abs/1705.06031) find
  47. WHO. 2020. Novel coronavirus(2019-ncov) situation report - 13. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200202-sitrep-13-ncov-v3.pdf [Accessed on June 30, 2020]. ↗
  48. Wikipedia. 2020. Misinformation Related to the COVID-19 pandemic. https://en.wikipedia.org/wiki/Misinformation related to the COVID-19 pandemic [Accessed on June 30, 2020]. ↗
  49. Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. A broad-coverage challenge corpus for sentence understanding through inference. In North American Chapter of the Association for Computational Linguistics (NAACL). find
  50. Kai-Chou Yang, Timothy Niven, and Hung-Yu Kao. 2019. Fake news detection as natural language inference. arXiv preprint arXiv:1907.07347. — [arXiv:1907.07347](https://arxiv.org/abs/1907.07347) find
  51. Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019. BERTScore: Evaluating Text Generation with BERT. arXiv preprint arXiv:1904.09675. find
  52. Arkaitz Zubiaga, Ahmet Aker, Kalina Bontcheva, Maria Liakata, and Rob Procter. 2018. Detection and resolution of rumours in social media: A survey. ACM Computing Surveys (CSUR), 51(2):1–36. find

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/hossain-2020-covidlies-detecting-covid-19-misinformation/