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Open Access

Analysis and Classification of Fake News Using Sequential Pattern Mining

College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen 518107, China
Department of Computer Science, Faculty of Computing and Information Technology, Univesity of Sargodha, Sargodha 40100, Pakistan
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Abstract

Disinformation, often known as fake news, is a major issue that has received a lot of attention lately. Many researchers have proposed effective means of detecting and addressing it. Current machine and deep learning based methodologies for classification/detection of fake news are content-based, network (propagation) based, or multimodal methods that combine both textual and visual information. We introduce here a framework, called FNACSPM, based on sequential pattern mining (SPM), for fake news analysis and classification. In this framework, six publicly available datasets, containing a diverse range of fake and real news, and their combination, are first transformed into a proper format. Then, algorithms for SPM are applied to the transformed datasets to extract frequent patterns (and rules) of words, phrases, or linguistic features. The obtained patterns capture distinctive characteristics associated with fake or real news content, providing valuable insights into the underlying structures and commonalities of misinformation. Subsequently, the discovered frequent patterns are used as features for fake news classification. This framework is evaluated with eight classifiers, and their performance is assessed with various metrics. Extensive experiments were performed and obtained results show that FNACSPM outperformed other state-of-the-art approaches for fake news classification, and that it expedites the classification task with high accuracy.

Keywords

classificationfake newsdisinformationsequential pattern mining (SPM)frequent patterns

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Big Data Mining and Analytics
Volume 7 Issue 3,
September 2024
Pages 942-963
DOI: 10.26599/BDMA.2024.9020015
Cite this article:
Nawaz MZ, Nawaz MS, Fournier-Viger P, et al. Analysis and Classification of Fake News Using Sequential Pattern Mining. Big Data Mining and Analytics, 2024, 7(3): 942-963. https://doi.org/10.26599/BDMA.2024.9020015

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Received: 28 January 2024
Accepted: 11 March 2024
Published: 28 August 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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