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[11]. In this paper, we present our framework for fake news detection and we discuss in detail a solution based on deep learning methodologies we implemented by leveraging Google Bert features. Our experiments conducted on two well-known and widely used real-world datasets suggest that our method can outperform the state-of-the-art approaches and allows fake news accurate detection, even in the case of limited content information.
A Deep Learning Approach to Fake News Detection / Masciari, E.; Moscato, V.; Picariello, A.; Sperli, G.. - 12117:(2020), pp. 113-122. (Intervento presentato al convegno 25th International Symposium on Methodologies for Intelligent Systems, ISMIS 2020 tenutosi a aut nel 2020) [10.1007/978-3-030-59491-6_11].
A Deep Learning Approach to Fake News Detection
Masciari E.;Moscato V.;Picariello A.;Sperli G.
2020
Abstract
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[11]. In this paper, we present our framework for fake news detection and we discuss in detail a solution based on deep learning methodologies we implemented by leveraging Google Bert features. Our experiments conducted on two well-known and widely used real-world datasets suggest that our method can outperform the state-of-the-art approaches and allows fake news accurate detection, even in the case of limited content information.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
