TY - GEN
T1 - Preventing Fake News Propagation in Social Networks Using a Context Trust-Based Security Model
AU - Voloch, Nadav
AU - Gudes, Ehud
AU - Gal-Oz, Nurit
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - Online Social Networks (OSN) security issues have been extensively researched in the past decade. Information is posted and shared by individuals and organizations in social networks in huge quantities. One of the most important non-resolved topics is the Fake News propagation problem. Fake news propagates because of several reasons, one of which is non-trustworthy users. These users, some with malicious intentions, and some with low social media awareness, are the ones actually spreading misleading information. As this occurs, other users, that are valid reliable users, are exposed to false 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 as a basis for the purpose of prevention of Fake News. We add context awareness and user profiling by analyzing the user’s activity in the network (posts, shares, etc.), and then use Machine Learning to detect these problematic users by analyzing data items that are fake or misleading. This addition creates a much more accurate picture of OSN users and their data and helps revealing the sources of the Fake News propagation and can prevent it. These aspects of the model create a strong reliable OSN data infrastructure.
AB - Online Social Networks (OSN) security issues have been extensively researched in the past decade. Information is posted and shared by individuals and organizations in social networks in huge quantities. One of the most important non-resolved topics is the Fake News propagation problem. Fake news propagates because of several reasons, one of which is non-trustworthy users. These users, some with malicious intentions, and some with low social media awareness, are the ones actually spreading misleading information. As this occurs, other users, that are valid reliable users, are exposed to false 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 as a basis for the purpose of prevention of Fake News. We add context awareness and user profiling by analyzing the user’s activity in the network (posts, shares, etc.), and then use Machine Learning to detect these problematic users by analyzing data items that are fake or misleading. This addition creates a much more accurate picture of OSN users and their data and helps revealing the sources of the Fake News propagation and can prevent it. These aspects of the model create a strong reliable OSN data infrastructure.
KW - Fake News detection
KW - Online social networks security
KW - Trust-based security models
UR - https://www.scopus.com/pages/publications/85123294403
U2 - 10.1007/978-3-030-92708-0_6
DO - 10.1007/978-3-030-92708-0_6
M3 - Conference contribution
AN - SCOPUS:85123294403
SN - 9783030927073
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 100
EP - 115
BT - Network and System Security - 15th International Conference, NSS 2021, Proceedings
A2 - Yang, Min
A2 - Chen, Chao
A2 - Liu, Yang
PB - Springer Science and Business Media Deutschland GmbH
T2 - 15th International Conference on Network and System Security, NSS 2021
Y2 - 23 October 2021 through 23 October 2021
ER -