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Recent years have witnessed the rise of misinformation campaigns that spreadspecific narratives on social media to manipulate public opinions on differentareas, such as politics and healthcare. Consequently, an effective andefficient automatic methodology to estimate the influence of the misinformationon user beliefs and activities is needed. However, existing works onmisinformation impact estimation either rely on small-scale psychologicalexperiments or can only discover the correlation between user behaviour andmisinformation. To address these issues, in this paper, we build up a causalframework that model the causal effect of misinformation from the perspectiveof temporal point process. To adapt the large-scale data, we design anefficient yet precise way to estimate the Individual Treatment Effect(ITE) vianeural temporal point process and gaussian mixture models. Extensiveexperiments on synthetic dataset verify the effectiveness and efficiency of ourmodel. We further apply our model on a real-world dataset of social media postsand engagements about COVID-19 vaccines. The experimental results indicate thatour model recognized identifiable causal effect of misinformation that hurtspeople's subjective emotions toward the vaccines.

Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Media