Detecting Fake News Spreaders in Social Networks using Inductive Representation Learning

Bhavtosh Rath, Aadesh Salecha, Jaideep Srivastava

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

An important aspect of preventing fake news dissemination is to proactively detect the likelihood of its spreading. Research in the domain of fake news spreader detection has not been explored much from a network analysis perspective. In this paper, we propose a graph neural network based approach to identify nodes that are likely to become spreaders of false information. Using the community health assessment model and interpersonal trust we propose an inductive representation learning framework to predict nodes of densely-connected community structures that are most likely to spread fake news, thus making the entire community vulnerable to the infection. Using topology and interaction based trust properties of nodes in real-world Twitter networks, we are able to predict false information spreaders with an accuracy of over 90%.

Original languageEnglish (US)
Title of host publicationProceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
EditorsMartin Atzmuller, Michele Coscia, Rokia Missaoui
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages182-189
Number of pages8
ISBN (Electronic)9781728110561
DOIs
StatePublished - Dec 7 2020
Event12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020 - Virtual, Online, Netherlands
Duration: Dec 7 2020Dec 10 2020

Publication series

NameProceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020

Conference

Conference12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
Country/TerritoryNetherlands
CityVirtual, Online
Period12/7/2012/10/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

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