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 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.

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