SCARLET: Explainable Attention Based Graph Neural Network for Fake News Spreader Prediction

Bhavtosh Rath, Xavier Morales, Jaideep Srivastava

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

Abstract

False information and true information fact checking it, often co-exist in social networks, each competing to influence people in their spread paths. An efficient strategy here to contain false information is to proactively identify if nodes in the spread path are likely to endorse false information (i.e. further spread it) or refutation information (thereby help contain false information spreading). In this paper, we propose SCARLET (truSt andCredibility bAsed gRaph neuraLnEtwork model using aTtention) to predict likely action of nodes in the spread path. We aggregate trust and credibility features from a node’s neighborhood using historical behavioral data and network structure and explain how features of a spreader’s neighborhood vary. Using real world Twitter datasets, we show that the model is able to predict false information spreaders with an accuracy of over 87%.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 25th Pacific-Asia Conference, PAKDD 2021, Proceedings
EditorsKamal Karlapalem, Hong Cheng, Naren Ramakrishnan, R. K. Agrawal, P. Krishna Reddy, Jaideep Srivastava, Tanmoy Chakraborty
PublisherSpringer Science and Business Media Deutschland GmbH
Pages714-727
Number of pages14
ISBN (Print)9783030757618
DOIs
StatePublished - 2021
Event25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021 - Virtual, Online
Duration: May 11 2021May 14 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12712 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021
CityVirtual, Online
Period5/11/215/14/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

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