Examining the Robustness of an Ensemble Learning Model for Credibility Based Fake News Detection

Ensemble learning is a technique of combining multiple base machine learning models and using the blended results as the final classification output. Such models provide a unique perspective on the classification results as it produces a more comprehensive and encompassing output. As such ensemble learning techniques are widely used for classification in today. Hence it is important that any ensemble learning model be robust and resilient to any type of data and not just applicable to one dataset. This research investigates and evaluates the robustness and the resilience of the proposed Legitimacy ensemble learning model. This ensemble learning model was previously proposed for Credibility Based Fake News Detection. This research evaluates Legitimacy’s performance with a variety of datasets. In the first scenario, the Legitimacy ensemble learning model is evaluated with 3 different binary classification datasets for training and testing purposes, respectively. In the second scenario, the Legitimacy model is assessed where one dataset is used for training whilst another dataset is used for testing. In the final scenario the Legitimacy ensemble learning model is evaluated against a multiclass dataset for multiclass classification. The results of all the above tests are assimilated and evaluated. The results suggest that the Legitimacy ensemble learning model performs well in all three scenarios giving AUC values all equal to or greater than 0.500. As such it can be concluded that the Legitimacy model is a robust and resilient ensemble learning technique and can be employed for the task of classification with any dataset.