Dataset from Zenodo · Memon, Shahan Ali
CMU-MisCov19: A Novel Twitter Dataset for Characterizing COVID-19 Misinformation
- License
- CC BY 4.0commercial use allowedLicense text
- This license allows commercial use. Usually you must credit the author; read the license for the exact terms.
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- ZIP, PDF
- Size
- 171 kB
- Updated
- Jul 19, 2024
Description
From conspiracy theories to fake cures and fake treatments, COVID-19 has become a hot-bed for the spread of misinformation online. It is more important than ever to identify methods to debunk and correct false information online. Detection and characterization of misinformation requires an availability of annotated datasets. Most of the published COVID-19 Twitter datasets are generic, lack annotations or labels, employ automated annotations using transfer learning or semi-supervised methods, or are not specifically designed for misinformation. Annotated datasets are either only focused on "fake news", are small in size, or have less diversity in terms of classes. Here, we present a novel Twitter misinformation dataset called "CMU-MisCov19" with 4573 annotated tweets over 17 themes around the COVID-19 discourse. We also present our annotation codebook for the different COVID-19 themes on Twitter, along with their descriptions and examples, for the community to use for collecting further annotations. Further details related to the dataset, and our analysis based on this dataset can be found at https://arxiv.org/abs/2008.00791 . In adherence to the Twitter’s terms and…
Description from Zenodo, shortened.
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