Leveraging Machine Learning for Fake News Detection

Elio Masciari, Vincenzo Moscato, Antonio Picariello, Giancarlo Sperlì


The uncontrolled growth of fake news creation and dissemination we observed in recent years causes continuous threats to democracy, justice, and public trust. This problem has significantly driven the effort of both academia and industries for developing more accurate fake news detection strategies. Early detection of fake news is crucial, however the availability of information about news propagation is limited. Moreover, it has been shown that people tend to believe more fake news due to their features (Vosoughi et al., 2018). In this paper, we present our complete framework for fake news detection and we discuss in detail a solution based on machine learning. Our experiments conducted on two well-known and widely used real-world datasets suggest that our settings can outperform the state-of-the-art approaches and allows fake news accurate detection, even in the case of limited content information.


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