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 [10]. In this paper, we present our framework for fake news detection and we discuss in detail an approach based on deep learning that we implemented by using 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.
Detecting fake news by image analysis / Masciari, E.; Moscato, V.; Picariello, A.; Sperli, G.. - (2020), pp. 1-5. (Intervento presentato al convegno 24th International Database Engineering and Applications Symposium, IDEAS 2020 tenutosi a kor nel 2020) [10.1145/3410566.3410599].
Detecting fake news by image analysis
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 [10]. In this paper, we present our framework for fake news detection and we discuss in detail an approach based on deep learning that we implemented by using 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.
