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Pathogenic Social Media (PSM) accounts such as terrorist supporter accountsand fake news writers have the capability of spreading disinformation to viralproportions. Early detection of PSM accounts is crucial as they are likely tobe key users to make malicious information "viral". In this paper, we adopt thecausal inference framework along with graph-based metrics in order todistinguish PSMs from normal users within a short time of their activities. Wepropose both supervised and semi-supervised approaches without taking thenetwork information and content into account. Results on a real-world datasetfrom Twitter accentuates the advantage of our proposed frameworks. We show ourapproach achieves 0.28 improvement in F1 score over existing approaches withthe precision of 0.90 and F1 score of 0.63.

An End-to-End Framework to Identify Pathogenic Social Media Accounts on Twitter