Fake News Detection Using Machine Learning: An Evaluation of Classification Models
Keywords:
Fake news detection, Long Short-Term Memory (LSTM), Machine learning, Naive bayes, Natural language processing, Random forest, Support Vector Machine (SVM)Abstract
Social media fake news attacks free speech and makes people question the media. This heavily affects elections and public health. Human fact-checkers cannot keep up with the amount of content posted on the web. Because of this, there is great interest in the use of Machine Learning (ML) for news verification. This paper surveys five ML techniques used for fake news verification: Naïve Bayes, Logistic Regression, Support Vector Machine (SVM), Random Forest, and Long Short-Term Memory (LSTM). The effectiveness of the techniques is compared using accuracy, precision, recall, and F1-score based on previous studies performed on three commonly used benchmark datasets. The survey shows that with the right number of resources, LSTM can provide the best accuracy, while Naïve Bayes and Support Vector Machine (SVM) are the best and most resource-efficient algorithms. The paper lists the limitations of existing systems such as the lack of real-time verification, the difficulty of detection in a different context (domain), and the sparseness of labelled training data.
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