Cargando…
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...
Autores principales: | , , , |
---|---|
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 |
_version_ | 1783714709153775616 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8237334 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT duliziaarianna fakenewsdetectionasurveyofevaluationdatasets AT cascheramariachiara fakenewsdetectionasurveyofevaluationdatasets AT ferrifernando fakenewsdetectionasurveyofevaluationdatasets AT grifonipatrizia fakenewsdetectionasurveyofevaluationdatasets |