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Dataset . 2020
License: CC BY
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ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2020
License: CC BY
Data sources: ZENODO
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FakeCovid- A Multilingual Cross domain Fact Check Dataset for COVID-19

Authors: Shahi, Gautam Kishore; Nandini, Durgesh;

FakeCovid- A Multilingual Cross domain Fact Check Dataset for COVID-19

Abstract

FakeCovid is the first multilingual cross-domain dataset of 7623 fact-checked news articles for COVID-19, collected from 04/01/2020 to 01/07/2020. We have collected the fact-checked articles from 92 fact-checking websites after obtaining references from Poynter and Snopes. We have manually annotated the collected articles into 11 categories of the fact-checked news according to their content. We ultimately generated dataset is in 40 languages from 105 countries.

{"references": ["Shahi, Gautam Kishore, and Durgesh Nandini. \"FakeCovid--A Multilingual Cross-domain Fact Check News Dataset for COVID-19.\" arXiv preprint arXiv:2006.11343 (2020)"]}

Keywords

Coronavirus, Fake News, COVID19, Fact check, multilingual, misinformation

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
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1
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75