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March 1, 2025Open Access

Towards Robust Models for Fake News Detection in Spanish

Sergio Gómez-González Paolo Rosso María Coll-Ardanuy

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Key Points

The study aims to enhance the detection of fake news in Spanish by developing robust models that adapt to changing themes.
Applied various data exploitation and augmentation techniques for model improvement.
Compared a simple transformer-based classifier with a large generative language model.
Utilized two dataset partitions: a standard one and a more adversarial partition for effectiveness comparison.
The augmented model demonstrated improved robustness over baseline classifiers in adversarial contexts.
Comparison showed that the generative model outperformed simpler approaches in handling diverse news topics.

Implication

Assesses model robustness in fake news detection in Spanish, suggesting improvements through data techniques.

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Cite This Study

Gómez-González et al. (2025) studied this question.

synapsesocial.com/papers/6a2a503380c8f91e7f39cd6e https://doi.org/10.26342/2025-74-6

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