Over the last few years, the phenomenon of fake news has become an important issue, especially during the worldwide COVID-19 pandemic, and also a serious risk for the public health. Due to the huge amount of information that is produced by the social media such as Facebook and Twitter it is becoming difficult to check the produced contents manually. This study proposes an automatic fake news detection system that supports or disproves the dubious claims while returning a set of documents from verified sources. The system is composed of multiple modules and it makes use of different techniques from machine learning, deep learning and natural language processing. Such techniques are used for the selection of relevant documents, to find among those, the ones that are similar to the tested claim and their stances. The proposed system will be used to check medical news and, in particular, the trustworthiness of posts related to the COVID-19 pandemic, vaccine and cure

An Explainable Fake News Detector Based on Named Entity Recognition and Stance Classification Applied to COVID-19 / DE MAGISTRIS, Giorgio; Russo, Samuele; Roma, Paolo; Starczewski, Janusz T.; Napoli, Christian. - In: INFORMATION. - ISSN 2078-2489. - 13:3(2022). [10.3390/info13030137]

An Explainable Fake News Detector Based on Named Entity Recognition and Stance Classification Applied to COVID-19

Giorgio De Magistris
Co-primo
Investigation
;
Samuele Russo
Co-primo
Conceptualization
;
Paolo Roma
Penultimo
Validation
;
Christian Napoli
Ultimo
Supervision
2022

Abstract

Over the last few years, the phenomenon of fake news has become an important issue, especially during the worldwide COVID-19 pandemic, and also a serious risk for the public health. Due to the huge amount of information that is produced by the social media such as Facebook and Twitter it is becoming difficult to check the produced contents manually. This study proposes an automatic fake news detection system that supports or disproves the dubious claims while returning a set of documents from verified sources. The system is composed of multiple modules and it makes use of different techniques from machine learning, deep learning and natural language processing. Such techniques are used for the selection of relevant documents, to find among those, the ones that are similar to the tested claim and their stances. The proposed system will be used to check medical news and, in particular, the trustworthiness of posts related to the COVID-19 pandemic, vaccine and cure
2022
natural language processing; named entity recognition; CNN; fake news; COVID-19; vaccines; explainable artificial intelligence
01 Pubblicazione su rivista::01a Articolo in rivista
An Explainable Fake News Detector Based on Named Entity Recognition and Stance Classification Applied to COVID-19 / DE MAGISTRIS, Giorgio; Russo, Samuele; Roma, Paolo; Starczewski, Janusz T.; Napoli, Christian. - In: INFORMATION. - ISSN 2078-2489. - 13:3(2022). [10.3390/info13030137]
File allegati a questo prodotto
File Dimensione Formato  
DeMagistris_An-Explainable_2022.pdf

accesso aperto

Note: https://doi.org/10.3390/info13030137
Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.94 MB
Formato Adobe PDF
1.94 MB Adobe PDF

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1625134
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 32
  • ???jsp.display-item.citation.isi??? 16
social impact