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arXiv Machine Learning By Yipeng Qian, Pengjie Zhao, Chaoxi Niu

CSC: Calibrated Simplicity for Conflict-Aware Social Bot Detection in the LLM Era

Read the original on arXiv Machine Learning →

The paper introduces CSC, a calibrated-simplicity framework for detecting social bots in the era of large language models. CSC combines a simplified prototype-guided graph expert, calibrated simplex-constrained fusion, and a lightweight inconsistency expert to address modality conflict between semantic and structural signals. Experiments on TwiBot-22, TwiBot-20, and MGStBot-large demonstrate that CSC improves calibrated decision quality while maintaining competitive performance across benchmarks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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