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Fake news detection: a survey of evaluation datasets

Fake news detection has gained increasing importance among the research community due to the widespread diffusion of fake news through media platforms. Many dataset have been released in the last few years, aiming to assess the performance of fake news detection methods. In this survey, we systemati...

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Detalles Bibliográficos
Autores principales: D’Ulizia, Arianna, Caschera, Maria Chiara, Ferri, Fernando, Grifoni, Patrizia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8237334/
https://www.ncbi.nlm.nih.gov/pubmed/34239967
http://dx.doi.org/10.7717/peerj-cs.518
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author D’Ulizia, Arianna
Caschera, Maria Chiara
Ferri, Fernando
Grifoni, Patrizia
author_facet D’Ulizia, Arianna
Caschera, Maria Chiara
Ferri, Fernando
Grifoni, Patrizia
author_sort D’Ulizia, Arianna
collection PubMed
description Fake news detection has gained increasing importance among the research community due to the widespread diffusion of fake news through media platforms. Many dataset have been released in the last few years, aiming to assess the performance of fake news detection methods. In this survey, we systematically review twenty-seven popular datasets for fake news detection by providing insights into the characteristics of each dataset and comparative analysis among them. A fake news detection datasets characterization composed of eleven characteristics extracted from the surveyed datasets is provided, along with a set of requirements for comparing and building new datasets. Due to the ongoing interest in this research topic, the results of the analysis are valuable to many researchers to guide the selection or definition of suitable datasets for evaluating their fake news detection methods.
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spelling pubmed-82373342021-07-07 Fake news detection: a survey of evaluation datasets D’Ulizia, Arianna Caschera, Maria Chiara Ferri, Fernando Grifoni, Patrizia PeerJ Comput Sci Data Science Fake news detection has gained increasing importance among the research community due to the widespread diffusion of fake news through media platforms. Many dataset have been released in the last few years, aiming to assess the performance of fake news detection methods. In this survey, we systematically review twenty-seven popular datasets for fake news detection by providing insights into the characteristics of each dataset and comparative analysis among them. A fake news detection datasets characterization composed of eleven characteristics extracted from the surveyed datasets is provided, along with a set of requirements for comparing and building new datasets. Due to the ongoing interest in this research topic, the results of the analysis are valuable to many researchers to guide the selection or definition of suitable datasets for evaluating their fake news detection methods. PeerJ Inc. 2021-06-18 /pmc/articles/PMC8237334/ /pubmed/34239967 http://dx.doi.org/10.7717/peerj-cs.518 Text en © 2021 D'Ulizia et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Data Science
D’Ulizia, Arianna
Caschera, Maria Chiara
Ferri, Fernando
Grifoni, Patrizia
Fake news detection: a survey of evaluation datasets
title Fake news detection: a survey of evaluation datasets
title_full Fake news detection: a survey of evaluation datasets
title_fullStr Fake news detection: a survey of evaluation datasets
title_full_unstemmed Fake news detection: a survey of evaluation datasets
title_short Fake news detection: a survey of evaluation datasets
title_sort fake news detection: a survey of evaluation datasets
topic Data Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8237334/
https://www.ncbi.nlm.nih.gov/pubmed/34239967
http://dx.doi.org/10.7717/peerj-cs.518
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