Version 1
: Received: 10 September 2024 / Approved: 11 September 2024 / Online: 11 September 2024 (07:32:27 CEST)
How to cite:
Lupei, M.; Shliakhta, M. TMining: Detecting Fake News with Machine Learning and Explainable AI. Preprints2024, 2024090847. https://doi.org/10.20944/preprints202409.0847.v1
Lupei, M.; Shliakhta, M. TMining: Detecting Fake News with Machine Learning and Explainable AI. Preprints 2024, 2024090847. https://doi.org/10.20944/preprints202409.0847.v1
Lupei, M.; Shliakhta, M. TMining: Detecting Fake News with Machine Learning and Explainable AI. Preprints2024, 2024090847. https://doi.org/10.20944/preprints202409.0847.v1
APA Style
Lupei, M., & Shliakhta, M. (2024). TMining: Detecting Fake News with Machine Learning and Explainable AI. Preprints. https://doi.org/10.20944/preprints202409.0847.v1
Chicago/Turabian Style
Lupei, M. and Myroslav Shliakhta. 2024 "TMining: Detecting Fake News with Machine Learning and Explainable AI" Preprints. https://doi.org/10.20944/preprints202409.0847.v1
Abstract
The spread of false information can significantly harm public opinion,underscoring the importance of accurately identifying untrustworthy news. This paperpresents an innovative machine learning (ML) tool, TMining, designed to evaluatenews credibility and facilitate various text-mining tasks. By examining a range of MLmethodologies alongside preprocessing techniques, we aim to boost the system'seffectiveness. Our research meticulously assesses different datasets, highlights theimpact of applying stemming techniques, and employs Local InterpretableModel-Agnostic Explanations (LIMEs) to shed light on the rationale behind modelpredictions. The outcomes reveal a notable enhancement in both the precision andclarity of the news verification process. The ultimate version of the model has beenmade available as an Application Program Interface (API), and its source code hasbeen shared openly, encouraging further exploration and collaboration within thescientific community. This initiative advances our ability to discern manipulativecontext from fictitious content and promotes transparency and understanding in thedomain of ML applications.
Keywords
Text Mining; Fake News; Model Explanation; Machine Learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
