TY - GEN
T1 - Fake News Detection in Social Networks Using Machine Learning and Trust
AU - Voloch, Nadav
AU - Gudes, Ehud
AU - Gal-Oz, Nurit
AU - Mitrany, Rotem
AU - Shani, Ofri
AU - Shoel, Maayan
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - Fake news propagation is a major challenge for Online Social Networks (OSN) security, which is not yet resolved. Fake news propagates because of several reasons, one of which is non-trustworthy users. Non-trustworthy users are those who spread misleading information either for malicious intentions or innocently as they lack social media awareness. As a result, they expose their sub networks to false or inaccurate information. In our previous research we have devised a comprehensive Trust-based model that can handle this problem from the user Trust aspect. The model involves Access Control for the direct circle of friends and Flow Control for the friends’ networks. In this paper we use this model and extend it for the purpose of preventing Fake News. We analyze user’s activity in the network (posts, shares, etc.) to learn their contexts. Using Machine Learning methods on data items that are fake or misleading, we detect suspicious users. This addition facilitates a much more accurate mapping of OSN users and their data which enables the identification of the Fake News propagation source. The extended model can be used to create a strong and reliable data infrastructure for OSN.
AB - Fake news propagation is a major challenge for Online Social Networks (OSN) security, which is not yet resolved. Fake news propagates because of several reasons, one of which is non-trustworthy users. Non-trustworthy users are those who spread misleading information either for malicious intentions or innocently as they lack social media awareness. As a result, they expose their sub networks to false or inaccurate information. In our previous research we have devised a comprehensive Trust-based model that can handle this problem from the user Trust aspect. The model involves Access Control for the direct circle of friends and Flow Control for the friends’ networks. In this paper we use this model and extend it for the purpose of preventing Fake News. We analyze user’s activity in the network (posts, shares, etc.) to learn their contexts. Using Machine Learning methods on data items that are fake or misleading, we detect suspicious users. This addition facilitates a much more accurate mapping of OSN users and their data which enables the identification of the Fake News propagation source. The extended model can be used to create a strong and reliable data infrastructure for OSN.
KW - Fake News detection
KW - Online social networks security
KW - Trust-based security models
UR - https://www.scopus.com/pages/publications/85134182718
U2 - 10.1007/978-3-031-07689-3_14
DO - 10.1007/978-3-031-07689-3_14
M3 - Conference contribution
AN - SCOPUS:85134182718
SN - 9783031076886
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 180
EP - 188
BT - Cyber Security, Cryptology, and Machine Learning - 6th International Symposium, CSCML 2022, Proceedings
A2 - Dolev, Shlomi
A2 - Meisels, Amnon
A2 - Katz, Jonathan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022
Y2 - 30 June 2022 through 1 July 2022
ER -