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arXiv · 2609.26279

FISSION: Label Augmentation for Bot Detection

Sen Yang·Ignacy Nieweglowski·Aviv Yaish
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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.

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Sen Yang, Ignacy Nieweglowski, Aviv Yaish. 2026-08-14. FISSION: Label Augmentation for Bot Detection. https://arxiv.org/abs/2609.26279

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