FISSION: Label Augmentation for Bot Detection

Abstract:Bot accounts and coordinated influence operations are often discovered via heuristic methods, leaving a dearth of reliable ground-truth labels for training detection systems. To address this challenge, we study a natural question: can we generate labels to assist in learning embeddings in which bots and accounts from the same coordinated operation are close? We present FISSION, a method to generate labels by splitting each account's activity into positively labeled sub-accounts. Given this label source, we train detection models which preserve behavioral regularities recurring across positive sub-accounts. We evaluate FISSION and show it outperforms prior methods in detecting Wikipedia sockpuppets and Twitter/X bots.

Artificial Intelligence (cs.AI)

arXiv:2609.26279 [cs.AI]

(or arXiv:2609.26279v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.26279 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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