Fighting post-truth using natural language processing: A review and open challenges

Please use this identifier to cite or link to this item:
Información del item - Informació de l'item - Item information
Title: Fighting post-truth using natural language processing: A review and open challenges
Authors: Saquete Boró, Estela | Tomás, David | Moreda, Paloma | Martínez-Barco, Patricio | Palomar, Manuel
Research Group/s: Procesamiento del Lenguaje y Sistemas de Información (GPLSI)
Center, Department or Service: Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos
Keywords: Natural language processing | Fake news | Post-truth | Deception detection | Automatic fact-checking | Clickbait detection | Stance detection | Credibility | Human language technologies | Applied computing | Document management and text processing | Document capture | Document analysis
Knowledge Area: Lenguajes y Sistemas Informáticos
Issue Date: 1-Mar-2020
Publisher: Elsevier
Citation: Expert Systems with Applications. 2020, 141: 112943. doi:10.1016/j.eswa.2019.112943
Abstract: Post-truth is a term that describes a distorting phenomenon that aims to manipulate public opinion and behavior. One of its key engines is the spread of Fake News. Nowadays most news is rapidly disseminated in written language via digital media and social networks. Therefore, to detect fake news it is becoming increasingly necessary to apply Artificial Intelligence (AI) and, more specifically Natural Language Processing (NLP). This paper presents a review of the application of AI to the complex task of automatically detecting fake news. The review begins with a definition and classification of fake news. Considering the complexity of the fake news detection task, a divide-and-conquer methodology was applied to identify a series of subtasks to tackle the problem from a computational perspective. As a result, the following subtasks were identified: deception detection; stance detection; controversy and polarization; automated fact checking; clickbait detection; and, credibility scores. From each subtask, a PRISMA compliant systematic review of the main studies was undertaken, searching Google Scholar. The various approaches and technologies are surveyed, as well as the resources and competitions that have been involved in resolving the different subtasks. The review concludes with a roadmap for addressing the future challenges that have emerged from the analysis of the state of the art, providing a rich source of potential work for the research community going forward.
Sponsor: This research work has been partially funded by Generalitat Valenciana through project “SIIA: Tecnologias del lenguaje humano para una sociedad inclusiva, igualitaria, y accesible” with grant reference PROMETEU/2018/089 , by the Spanish Government through project RTI2018-094653-B-C22: “Modelado Del Comportamiento de Entidades Digitales Mediante Tecnologias Del Lenguaje Humano”, as well as by the project “Analisis de Sentimientos Aplicado a la Prevencion del Suicidio en las Redes Sociales (ASAP)” funded by Ayudas Fundacion BBVA a equipos de investigacion cientifica.
ISSN: 0957-4174 (Print) | 1873-6793 (Online)
DOI: 10.1016/j.eswa.2019.112943
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2019 Elsevier Ltd.
Peer Review: si
Publisher version:
Appears in Collections:INV - GPLSI - Artículos de Revistas

Files in This Item:
Files in This Item:
File Description SizeFormat 
Thumbnail2020_Saquete_etal_ESWA_final.pdfVersión final (acceso restringido)1,41 MBAdobe PDFOpen    Request a copy
Thumbnail2020_Saquete_etal_ESWA_accepted.pdfAccepted Manuscript (acceso abierto)7,94 MBAdobe PDFOpen Preview

Items in RUA are protected by copyright, with all rights reserved, unless otherwise indicated.