Fighting disinformation with artificial intelligence

Fundamentals, advances and challenges

Datos Bibliográficos

ID12623866
AutoresAndrés Montoro-Montarroso (0000-0003-1893-3346, Universidad de Granada, autor de correspondencia), Javier Cantón-Correa (0000-0002-8466-1679, Universidad de Granada), Paolo Rosso (0000-0002-8922-1242, Universitat Politècnica de València), Berta Chulvi (0000-0003-1169-0978, Universitat Politècnica de València), Ángel Panizo-Lledot (0000-0002-2195-3527, Universidad Politécnica de Madrid), Javier Huertas‐Tato (0000-0003-4127-5505, Universidad Politécnica de Madrid), Blanca Calvo Figueras (0000-0001-6939-3576, Barcelona Supercomputing Center), María José Rementeria (0000-0002-3140-1160, Barcelona Supercomputing Center), Juan Gómez‐Romero (0000-0003-0439-3692, Universidad de Granada)
Año2023
Fecha de publicación2023-06-17
Open AccessSí
TipoARTICLE
RevistaEl Profesional de la Informacion (JOURNAL)
Identificadores de la revistaISSN: 1386-6710 • E-ISSN: 1699-2407
EditorialEdiciones Profesionales de la Informacion SL (PUBLISHER)
DOI10.3145/epi.2023.may.22
OpenAlexW4380904852
IdiomaEN
Citas recibidas15
Referencias citadas95

Internet and social media have revolutionised the way news is distributed and consumed. However, the constant flow of massive amounts of content has made it difficult to discern between truth and falsehood, especially in online platforms plagued with malicious actors who create and spread harmful stories. Debunking disinformation is costly, which has put artificial intelligence (AI) and, more specifically, machine learning (ML) in the spotlight as a solution to this problem. This work revises recent literature on AI and ML techniques to combat disinformation, ranging from automatic classification to feature extraction, as well as their role in creating realistic synthetic content. We conclude that ML advances have been mainly focused on automatic classification and scarcely adopted outside research labs due to their dependence on limited-scope datasets. Therefore, research efforts should be redirected towards developing AI-based systems that are reliable and trustworthy in supporting humans in early disinformation detection instead of fully automated solutions

Computer security · Data science · Disinformation · Scope (computer science · Social media · The Internet · Trustworthiness · World Wide Web · Advanced Malware Detection Techniques · Computer Science · Misinformation and Its Impacts · Spam and Phishing Detection · Artificial Intelligence

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