Autores
Damián Sandoval Sergio Arturo
Calvo Castro Francisco Hiram
Gelbukh Alexander
Título Fake News detection using n-grams for PAN@CLEF competition
Tipo Revista
Sub-tipo JCR
Descripción Journal of Intelligent & Fuzzy Systems
Resumen The paper presents a classifier for fake news spreaders detection in social media. Detecting fake news spreaders is an important task because this kind of disinformation aims to change the reader's opinion about a relevant topic for the society. This work presents a classifier that can compete with the ones that are found in the state-of-the-art. In addition, this work applies Explainable Artificial Intelligence (XIA) methods in order to understand the corpora used and how the model estimates results. The work focuses on the corpora developed by members of the PAN@CLEF 2020 competition. The score obtained surpasses the state-of-the-art with a mean accuracy score of 0.7825. The solution uses XIA methods for the feature selection process, since they present more stability to the selection than most of traditional feature selection methods. Also, this work concludes that the detection done by the solution approach is generally based on the topic of the text. © 2022 - IOS Press. All rights reserved.
Observaciones DOI 10.3233/JIFS-219251
Lugar Amsterdam
País Paises Bajos
No. de páginas 4633-4640
Vol. / Cap. v. 42 no. 5
Inicio 2022-03-31
Fin
ISBN/ISSN