Fake News Detection in Social Networks Using Machine Learning and Trust

Nadav Voloch, Ehud Gudes, Nurit Gal-Oz, Rotem Mitrany, Ofri Shani, Maayan Shoel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationCyber Security, Cryptology, and Machine Learning - 6th International Symposium, CSCML 2022, Proceedings
EditorsShlomi Dolev, Amnon Meisels, Jonathan Katz
PublisherSpringer Science and Business Media Deutschland GmbH
Pages180-188
Number of pages9
ISBN (Print)9783031076886
DOIs
StatePublished - 1 Jan 2022
Event6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022 - Beer Sheva, Israel
Duration: 30 Jun 20221 Jul 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13301 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022
Country/TerritoryIsrael
CityBeer Sheva
Period30/06/221/07/22

Keywords

  • Fake News detection
  • Online social networks security
  • Trust-based security models

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

Fingerprint

Dive into the research topics of 'Fake News Detection in Social Networks Using Machine Learning and Trust'. Together they form a unique fingerprint.

Cite this