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Fighting disinformation with artificial intelligence: fundamentals, advances and challenges

  • Montoro-Montarroso, Andrés [1] ; Cantón-Correa, Javier [5] ; Rosso, Paolo [2] ; Chulvi, Berta [2] ; Panizo-Lledot, Ángel [3] ; Huertas-Tato, Javier [3] ; Calvo-Figueras, Blanca [4] ; Rementeria, M. José [4] ; Gómez-Romero, Juan [1]
    1. [1] Universidad de Granada

      Universidad de Granada

      Granada, España

    2. [2] Universidad Politécnica de Valencia

      Universidad Politécnica de Valencia

      Valencia, España

    3. [3] Universidad Politécnica de Madrid

      Universidad Politécnica de Madrid

      Madrid, España

    4. [4] Centro Nacional de Supercomputación

      Centro Nacional de Supercomputación

      Barcelona, España

    5. [5] Universidad de Granada / Universidad Internacional de La Rioja
    Mostrar afiliaciones +
  • Localización: El profesional de la información, ISSN-e 1699-2407, ISSN 1386-6710, Vol. 32, Nº 3, 2023 (Ejemplar dedicado a: Network activisms)
  • Idioma: inglés
  • DOI: 10.3145/epi.2023.may.22
  • Títulos paralelos:
    • Inteligencia artificial contra la desinformación: fundamentos, avances y retos
  • Enlaces
  • Resumen
    • español

      Internet y las redes sociales han revolucionado la forma en la que se distribuye y consume la información. Sin embargo, la enorme cantidad de contenidos disponibles en estas plataformas dificulta la tarea distinguir entre lo verdadero y lo falso, más aún con la proliferación de actores malintencionados que difunden bulos. Desmentir la desinformación es un proceso muy costoso, por lo que en los últimos años se han desarrollado múltiples investigaciones sobre el potencial de la inteligencia artificial (IA) –y, más concretamente, del aprendizaje automático (AA)– como una solución al problema. Este trabajo revisa la bibliografía reciente sobre las técnicas de IA y AA que han sido propuestas para combatir la desinformación, que van desde la clasificación automática de texto hasta la extracción de características, así como el papel relevante que pueden jugar en la creación de contenido artificial. La principal conclusión del estudio es que los avances en IA se han centrado principalmente en la clasificación automática y que su utilización fuera de los laboratorios de investigación ha sido escasa. Esto se debe principalmente a que los modelos de AA dependen mucho de los conjuntos de datos con los que son entrenados, lo cual limita su aplicación y su efectividad en diferentes ámbitos. En consecuencia, se propone que los esfuerzos de investigación ha de dirigirse hacia el desarrollo de sistemas de IA que sean explicables, confiables y que apoyen a las personas, en lugar de sustituirlas, en la detección temprana de desinformación.

    • English

      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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