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Online social network platforms have a problem with misinformation. Onepopular way of addressing this problem is via the use of machine learning basedautomated misinformation detection systems to classify if a post ismisinformation. Instead of post hoc detection, we propose to predict if a userwill engage with misinformation in advance and design an effective graph neuralnetwork classifier based on ego-graphs for this task. However, social networksare highly dynamic, reflecting continual changes in user behaviour, as well asthe content being posted. This is problematic for machine learning models whichare typically trained on a static training dataset, and can thus becomeoutdated when the social network changes. Inspired by the success of continuallearning on such problems, we propose an ego-graphs replay strategy incontinual learning (EgoCL) using graph neural networks to effectively addressthis issue. We have evaluated the performance of our method on user engagementwith misinformation on two Twitter datasets across nineteen misinformation andconspiracy topics. Our experimental results show that our approach EgoCL hasbetter performance in terms of predictive accuracy and computational resourcesthan the state of the art.

Ego-graph Replay based Continual Learning for Misinformation Engagement Prediction