Unmasking Fake News: A Naïve Bayes Classifier Approach to Combat Misinformation
Paper
Paper/Presentation Title | Unmasking Fake News: A Naïve Bayes Classifier Approach to Combat Misinformation |
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Presentation Type | Paper |
Authors | Sahi, Aqeel, Hayawi, Mustafa, Lafta, Raid, Abdulla, Shahab and Diykh, Mohammed |
Journal or Proceedings Title | Proceedings of the 4th International Conference on Innovations in Computing Research (ICR’25) |
Journal Citation | 1487, p. 118–125 |
Number of Pages | 8 |
Year | 2025 |
Publisher | Springer |
Place of Publication | Switzerland |
ISBN | 9783031956522 |
9783031956515 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-031-95652-2_11 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-031-95652-2_11 |
Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-3-031-95652-2 |
Conference/Event | 4th International Conference on Innovations in Computing Research (ICR’25) |
Event Details | 4th International Conference on Innovations in Computing Research (ICR’25) Delivery In person Event Date 25 to end of 27 Aug 2025 Event Location London, United Kingdom |
Abstract | Fake news has been a noticeable issue in the last few decades after the presence of the Internet. Many news channels, networks, and social media platforms provide us with news from around the globe. These news resources can also be used to share malicious and fake news. Therefore, detecting and handling this fake news is crucial since the world’s view is based on this information. Verifying news individually by a human being is completely unfeasible. Thus, we proposed an artificially intelligent Naïve Bayes prediction model that can help classify news and detect if a given news is fake or real. The proposed model used the Naïve Bayes classifier, which gives great results in text classifications such as spam filtering. This paper also includes an analysis of results and performance measurement. |
Keywords | Fake News; ML; Neural Network; Classification; Naïve Bayes |
Article Publishing Charge (APC) Funding | School/Centre |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460806. Human-computer interaction |
Public Notes | The accessible file is the accepted version of the paper. Please refer to the URL for the published version. |
Series | Lecture Notes in Networks and Systems |
Byline Affiliations | School of Mathematics, Physics and Computing |
Al-Shatrah University, Iraq | |
UniSQ College | |
University of Thi-Qar, Iraq |
https://research.usq.edu.au/item/zy6x3/unmasking-fake-news-a-na-ve-bayes-classifier-approach-to-combat-misinformation
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