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Fake news detection continues to be a major problem that affects our society today. Fake news can be classified using a variety of methods. Predicting and detecting fake news has proven to be challenging even for machine learning algorithms. This research employs Legitimacy, a unique ensemble machine learning model to accomplish the task of Credibility-Based Fake News Detection. The Legitimacy ensemble combines the learning potential of a Two-Class Boosted Decision Tree and a Two-Class Neural Network. The ensemble technique follows a pseudo-mixture-of-experts methodology. For the gating model, an instance of Two-Class Logistic Regression is implemented. This study validates Legitimacy using a standard dataset with features relating to the credibility of news publishers to predict fake news. These features are analysed using the ensemble algorithm. The results of these experiments are examined using four evaluation methodologies. The analysis of the results reveals positive performance with the use of the ensemble ML method with an accuracy of 96.9%. This ensemble’s performance is compared with the performance of the two base machine learning models of the ensemble. The performance of the ensemble surpasses that of the two base models. The performance of Legitimacy is also analysed as the size of the dataset increases to demonstrate its scalability. Hence, based on our selected dataset, the Legitimacy ensemble model has proven to be most appropriate for Credibility-Based Fake News Detection.

Mobile Adhoc Networks (MANETs) are utilised in a variety of mission critical situations and as such it is important to detect any fake news that exists in such networks. This research combines the power of Veracity, a unique, computational social system with that of Legitimacy, a dedicated ensemble learning technique, to detect Fake News in MANET Messaging. Veracity uses five algorithms namely, VerifyNews, CompareText, PredictCred, CredScore and EyeTruth for the capture, computation and analysis of the credibility and content data features using computational social intelligence. To validate Veracity, a dataset of publisher credibility-based and message content-based features is generated to predict fake news. To analyse the data features, Legitimacy, a unique ensemble learning prediction model is used. Four analytical methodologies are used to analyse these experimental results. The analysis of the results reports a good performance of the Veracity architecture combined with the Legitimacy model for the task of fake news detection in MANET Messaging.

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