Development of a machine learning algorithm for fake news detection

• Published in: Indonesian Journal of Electrical Engineering and Computer Science
• Main Author: Abdullah N.A.S.; Rusli N.I.A.; Yuslee N.S.
• Format: Article
• Language: English
• Published: Institute of Advanced Engineering and Science 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198090562&doi=10.11591%2fijeecs.v35.i3.pp1732-1743&partnerID=40&md5=4dbf62275f0cea4e88fae99a323e0641

With the extensive technological advancements and expansion, the persistent issues regarding the creation and rapid dissemination of fake news have become a prevalent and recurrent concern. The manipulation of news content has critical repercussions, such as causing public mistrust, fear, harm, and misinformation. Addressing that, this study developed a supervised machine learning algorithm that can accurately classify social media data as fake news. The methodology of the proposed fake news detection model involved five main components: data acquisition from Twitter, data preprocessing, data transformation, model development using Naïve Bayes, decision tree, and support vector machine (SVM) and model evaluation using accuracy, precision, recall and F1-score. The results revealed that decision tree recorded the highest accuracy for both textual data (100%) and metadata (94.54%) and consistently outperformed both Naïve Bayes and SVM in terms of precision, recall, and F1-score metrics, with a score of 100% for the classification of textual data-based datasets. Regarding the metadata-based classification, decision tree also demonstrated excellent performance, with the highest F1-score of 94% for fake news data. Meanwhile, SVM exhibited the highest precision and recall performance for the metadata-based classification. Overall, the application of the decision tree classifier was deemed the most effective in Twitter fake news detection. © 2024 Institute of Advanced Engineering and Science. All rights reserved.