An End-to-End Framework to Identify Pathogenic Social Media Accounts on Twitter
This work addresses the challenge of identifying malicious accounts to prevent viral disinformation spread, representing an incremental advance in social media security.
The paper tackled the problem of early detection of pathogenic social media accounts on Twitter, such as terrorist supporters and fake news writers, by proposing an end-to-end framework using causal inference and graph-based metrics, achieving a 0.28 improvement in F1 score over existing approaches with a precision of 0.90 and F1 score of 0.63.
Pathogenic Social Media (PSM) accounts such as terrorist supporter accounts and fake news writers have the capability of spreading disinformation to viral proportions. Early detection of PSM accounts is crucial as they are likely to be key users to make malicious information "viral". In this paper, we adopt the causal inference framework along with graph-based metrics in order to distinguish PSMs from normal users within a short time of their activities. We propose both supervised and semi-supervised approaches without taking the network information and content into account. Results on a real-world dataset from Twitter accentuates the advantage of our proposed frameworks. We show our approach achieves 0.28 improvement in F1 score over existing approaches with the precision of 0.90 and F1 score of 0.63.