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Journal of Intelligent Information Systems

Erschienen in:

23.12.2022

Towards a soft three-level voting model (Soft T-LVM) for fake news detection

verfasst von: Boutheina Jlifi, Chayma Sakrani, Claude Duvallet

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 1/2023

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Abstract

Fake news has a worldwide impact and the potential to change political scenarios and human behavior, especially in a critical time like the COVID-19 pandemic. This work suggests a Soft Three-Level Voting Model (Soft T-LVM) for automatically classifying COVID-19 fake news. We train different individual machine learning algorithms and different ensemble methods in order to overcome the weakness of individual models. This novel model is based on the soft-voting technique to calculate the class with the majority of votes and to choose the classifiers to merge and apply at every level. We use the Grid search method to tune the hyper-parameters during the process of classification and voting. The experimental evaluation confirms that our proposed model approach has superior performance compared to the other classifiers.

Vorheriger Artikel MhSa-GRU: combining user’s dynamic preferences and items’ correlation to augment sequence recommendation

Nächster Artikel Group event recommendation based on a heterogeneous attribute graph considering long- and short- term preferences

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Titel: Towards a soft three-level voting model (Soft T-LVM) for fake news detection
verfasst von: Boutheina Jlifi
Chayma Sakrani
Claude Duvallet
Publikationsdatum: 23.12.2022
Verlag: Springer US
Erschienen in: Journal of Intelligent Information Systems / Ausgabe 1/2023
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI: https://doi.org/10.1007/s10844-022-00769-7

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