Fighting disinformation with artificial intelligence: fundamentals, advances and challenges



Palabras clave:

Journalism, Disinformation, Computing, Artificial Intelligence, AI, Machine learning, Fact-checking, Datasets, Natural language processing, NLP, Social network analysis, Deep fakes, Large language models


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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Cómo citar

Montoro-Montarroso , A., Cantón-Correa, J., Rosso, P., Chulvi, B., Panizo-Lledot, Ángel, Huertas-Tato, J., Calvo-Figueras, B., Rementeria, M. J., & Gómez-Romero, J. (2023). Fighting disinformation with artificial intelligence: fundamentals, advances and challenges. Profesional De La información, 32(3).



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