City Research Online

Abstract

This study explores the decision-making processes of machine learning models for text classification problems. Using a fake news dataset as a test case, the study compares neural networks and machine learning approaches to fake news detection. This text-based problem has a feature space of 12,569 dimensions, and the necessity of the full dimensionality of the data is explored. In addition to developing effective classifiers, this study aims to investigate neural network interpretability by applying an explainable AI framework to extract human-understandable rules from trained models. The rule extraction process, taking a pedagogical approach, investigates the decision making of models. A Boolean function based model was developed, and the extent to which this rule-based system over a reduced feature set is successful is evaluated.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record will be available online at: https://link.springer.com/series/7899
Publisher Keywords:	Fake news detection, machine learning, explainable AI, natural language processing, rule extraction
Subjects:	P Language and Literature > P Philology. Linguistics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology School of Science & Technology > Department of Computer Science
SWORD Depositor:	Symplectic Administrator

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