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The Methods and Approaches of Explainable Artificial Intelligence Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Transformer Based Models in Fake News Detection

Authors : Sebastian Kula, Rafał Kozik, Michał Choraś, Michał Woźniak

Published in: Computational Science – ICCS 2021

Publisher: Springer International Publishing

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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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previous chapter Fake or Real? The Novel Approach to Detecting Online Disinformation Based on Multi ML Classifiers
next chapter Towards Model-Agnostic Ensemble Explanations
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Metadata
Title
Transformer Based Models in Fake News Detection
Authors
Sebastian Kula
Rafał Kozik
Michał Choraś
Michał Woźniak
Copyright Year
2021
Book
Computational Science – ICCS 2021

Print ISBN: 978-3-030-77969-6

Electronic ISBN: 978-3-030-77970-2

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-77970-2_3

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