COVIDLies: Detecting COVID-19 Misinformation on Social Media

Tamanna Hossain · Robert L. Logan · Dominic Arjuna Ugarte · Yoshitomo Matsubara · Sean D. Young · Sameer Singh

210 citations0/0 files storedWorkshop on NLP for COVID-19 (NLP-COVID19)

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Misinformation and Its Impacts Topic Modeling Spam and Phishing Detection

Abstract

The ongoing pandemic has heightened the need for developing tools to flag COVID-19related misinformation on the internet, specifically on social media such as Twitter.However, due to novel language and the rapid change of information, existing misinformation detection datasets are not effective for evaluating systems designed to detect misinformation on this topic.Misinformation detection can be divided into two sub-tasks: (i) retrieval of misconceptions relevant to posts being checked for veracity, and (ii) stance detection to identify whether the posts Agree, Disagree, or express No Stance towards the retrieved misconceptions.To facilitate research on this task, we release COVIDLIES 1 , a dataset of 6761 expert-annotated tweets to evaluate the performance of misinformation detection systems on 86 different pieces of COVID-19 related misinformation.We evaluate existing NLP systems on this dataset, providing initial benchmarks and identifying key challenges for future models to improve upon.

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References

2017Fake News Detection on Social Media 2011Information credibility on twitter 2020Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for a Scalable Accuracy-Nudge Intervention 2017"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection 2020FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media 2010Twitter under crisis 2020Coronavirus Goes Viral: Quantifying the COVID-19 Misinformation Epidemic on Twitter 2018Detection and Resolution of Rumours in Social Media

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2024Quantifying the impact of misinformation and vaccine-skeptical content on Facebook 2021CoAID-DEEP: An Optimized Intelligent Framework for Automated Detecting COVID-19 Misleading Information on Twitter 2021Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society 2022A Survey on Stance Detection for Mis- and Disinformation Identification 2021How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact

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