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

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	FakeCovid -- A Multilingual Cross-domain Fact Check News Dataset for COVID-19
Creator	Kishore, Gautam Nandini, Durgesh
source	ArXiv
abstract	In this paper, we present a first multilingual cross-domain dataset of 5182 fact-checked news articles for COVID-19, collected from 04/01/2020 to 15/05/2020. We have collected the fact-checked articles from 92 different fact-checking websites after obtaining references from Poynter and Snopes. We have manually annotated articles into 11 different categories of the fact-checked news according to their content. The dataset is in 40 languages from 105 countries. We have built a classifier to detect fake news and present results for the automatic fake news detection and its class. Our model achieves an F1 score of 0.76 to detect the false class and other fact check articles. The FakeCovid dataset is available at Github.
has issue date	2020-06-19(xsd:dateTime)
bibo:doi	10.36190/2020.14
has license	arxiv
sha1sum (hex)	7895137ad9283d3e62141c15502765f5d739035e
schema:url	https://doi.org/10.36190/2020.14
resource representing a document's title	FakeCovid -- A Multilingual Cross-domain Fact Check News Dataset for COVID-19
resource representing a document's body	covid:7895137ad9283d3e62141c15502765f5d739035e#body_text
is schema:about of	named entity 'references' named entity 'CLASS' named entity 'COVID-19' named entity 'PAPER' named entity 'CROSS' »more»

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