The uncontrolled growth of fake news creation and dissemi-nation we observed in recent years causes continuous threats to democ-racy, 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, how-ever 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 [13]. 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 fea-tures. 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.
Some experiments on Deep Learning for Fake News Detection / Chianese, A.; Masciari, E.; Moscato, V.; Picariello, A.; Sperli, G.. - 2646:(2020), pp. 108-115. (Intervento presentato al convegno 28th Italian Symposium on Advanced Database Systems, SEBD 2020 tenutosi a ita nel 2020).
Some experiments on Deep Learning for Fake News Detection
Chianese A.;Masciari E.;Moscato V.;Picariello A.;Sperli G.
2020
Abstract
The uncontrolled growth of fake news creation and dissemi-nation we observed in recent years causes continuous threats to democ-racy, 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, how-ever 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 [13]. 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 fea-tures. 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.
