Multi-Domain Fake News Detection Exploiting Ensemble Learning Techniques

Traditional fake news detection methods rely on machine learning models like Decision Trees, Random Forests, and SVM, utilizing features like word count and term frequency. These methods struggle to capture the nuanced features of fake news, especially in the case of evolving online content. To overcome these limitations, this paper provides a Multi-View Ensemble classifier which considers domain knowledge during classification, a critical feature for fake news detection. The multi-view approach allows the classifiers to identify patterns in different aspects, which might be missed by traditional methods. The proposed ensemble method utilizes a weighted voting strategy for combining the results from multiple classifiers. The introduction of domain knowledge allows the better generalization of classifiers, against the rapidly evolving domains of fake news. The weighted voting strategy proved to be much more efficient compared with other voting approaches and the achieved results surpassed those of a reference state-of-the-art model.