Fighting disinformation with artificial intelligencefundamentals, advances and challenges

  1. Montoro-Montarroso, Andrés 1
  2. Cantón-Correa, Javier 2
  3. Rosso, Paolo 3
  4. Chulvi, Berta 3
  5. Panizo-Lledot, Ángel 4
  6. Huertas-Tato, Javier 4
  7. Calvo-Figueras, Blanca 5
  8. Rementeria, M. José 5
  9. Gómez-Romero, Juan 1
  1. 1 Universidad de Granada
    info

    Universidad de Granada

    Granada, España

    ROR https://ror.org/04njjy449

  2. 2 Universidad de Granada / Universidad Internacional de La Rioja
  3. 3 Universidad Politécnica de Valencia
    info

    Universidad Politécnica de Valencia

    Valencia, España

    ROR https://ror.org/01460j859

  4. 4 Universidad Politécnica de Madrid
    info

    Universidad Politécnica de Madrid

    Madrid, España

    ROR https://ror.org/03n6nwv02

  5. 5 Centro Nacional de Supercomputación
    info

    Centro Nacional de Supercomputación

    Barcelona, España

    ROR https://ror.org/05sd8tv96

Revista:
El profesional de la información

ISSN: 1386-6710 1699-2407

Año de publicación: 2023

Título del ejemplar: Network activisms

Volumen: 32

Número: 3

Tipo: Artículo

DOI: 10.3145/EPI.2023.MAY.22 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: El profesional de la información

Resumen

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. 

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