Abstract
The article presents models for detecting fake news and the results of the analyzes of the application of these models. The precision, f1-score, recall metrics were proposed as a measure of the model quality assessment. Neural network architectures, based on the state-of-the-art solutions of the Transformer type were applied to create the models. The computing capabilities of the Google Colaboratory remote platform, as well as the Flair library, made it feasible to obtain reliable, robust models for fake news detection. The problem of disinformation and fake news is an important issue for modern societies, which commonly use state-of-the-art telecommunications technologies. Artificial intelligence and deep learning techniques are considered to be effective tools in protection against these undesirable phenomena.
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This work is supported by SocialTruth project (http://socialtruth.eu), which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825477.
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Kula, S., Kozik, R., Choraś, M., Woźniak, M. (2021). Transformer Based Models in Fake News Detection. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12745. Springer, Cham. https://doi.org/10.1007/978-3-030-77970-2_3
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