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Ego-graph replay based continual learning for misinformation engagement prediction

Bo, Hongbo; McConville, Ryan; Hong, Jun; Liu, Weiru

Authors

Hongbo Bo

Ryan McConville

Jun Hong Jun.Hong@uwe.ac.uk
Professor in Artificial Intelligence

Weiru Liu



Abstract

Online social network platforms have a problem with misinformation. One popular way of addressing this problem is via the use of machine learning based automated misinformation detection systems to classify if a post is misinformation. Instead of post hoc detection, we propose to predict if a user will engage with misinformation in advance and design an effective graph neural network classifier based on ego-graphs for this task. However, social networks are highly dynamic, reflecting continual changes in user behaviour, as well as the content being posted. This is problematic for machine learning models which are typically trained on a static training dataset, and can thus become outdated when the social network changes. Inspired by the success of continual learning on such problems, we propose an ego-graphs replay strategy in continual learning (EgoCL) using graph neural networks to effectively address this issue. We have evaluated the performance of our method on user engagement with misinformation on two Twitter datasets across nineteen misinformation and conspiracy topics. Our experimental results show that our approach EgoCL has better performance in terms of predictive accuracy and computational resources than the state of the art.

Presentation Conference Type Conference Paper (Published)
Conference Name 2022 International Joint Conference on Neural Networks (IJCNN)
Start Date Jul 18, 2022
End Date Jul 23, 2022
Acceptance Date May 2, 2022
Online Publication Date Sep 30, 2022
Publication Date Sep 30, 2022
Deposit Date Jun 27, 2023
Publicly Available Date Oct 1, 2024
Volume 2022-July
Pages 01-08
Series Title International Joint Conference on Neural Networks (IJCNN)
Series ISSN 2161-4407; 2161-4393
Book Title 2022 International Joint Conference on Neural Networks (IJCNN)
ISBN 978-1-6654-9526-4
DOI https://doi.org/10.1109/IJCNN55064.2022.9892557
Keywords Index Terms-Continual Learning; Graph Neural Networks; Social Networks; Misinformation
Public URL https://uwe-repository.worktribe.com/output/9884090
Publisher URL https://ieeexplore.ieee.org/document/9892557

Files

This file is under embargo until Oct 1, 2024 due to copyright reasons.

Contact Jun.Hong@uwe.ac.uk to request a copy for personal use.




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