The proliferation of fake news has raised concerns regarding its detection, posing a significant challenge. Motivated by the ongoing discussion on the sustainability of machine learning algorithms, this paper discusses the usefulness of data reduction for fake news detection. This is accomplished by using the fuzzy transform (or F -transform for short), which has already been proven effective, in the literature, to reduce the training time. A Long Short Term Memory architecture is then employed for classification to determine the authenticity of the news. From the formal perspective, we discuss in general the role of the F -transform in a learning system. Regarding the numerical experiments, we use five publicly available datasets, trying different compression ratios and different types of F -transform, assessing accuracy, F1 score, training time and energy consumption with and without F -transform. Although the F -transform is a lossy compression technique, the results show a negligible variation in accuracy and F1 -score when comparing results with and without F -transform (i.e. 1%- 3% in most cases and around 10% in one case). This seems to be congruent with the theoretical achievement. Furthermore, the approach yields substantial training time and energy savings, with over 50% reduction in energy consumption.

Using fuzzy transform for sustainable fake news detection

Loia V.;Tomasiello S.
2024-01-01

Abstract

The proliferation of fake news has raised concerns regarding its detection, posing a significant challenge. Motivated by the ongoing discussion on the sustainability of machine learning algorithms, this paper discusses the usefulness of data reduction for fake news detection. This is accomplished by using the fuzzy transform (or F -transform for short), which has already been proven effective, in the literature, to reduce the training time. A Long Short Term Memory architecture is then employed for classification to determine the authenticity of the news. From the formal perspective, we discuss in general the role of the F -transform in a learning system. Regarding the numerical experiments, we use five publicly available datasets, trying different compression ratios and different types of F -transform, assessing accuracy, F1 score, training time and energy consumption with and without F -transform. Although the F -transform is a lossy compression technique, the results show a negligible variation in accuracy and F1 -score when comparing results with and without F -transform (i.e. 1%- 3% in most cases and around 10% in one case). This seems to be congruent with the theoretical achievement. Furthermore, the approach yields substantial training time and energy savings, with over 50% reduction in energy consumption.
2024
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4859455
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