Concurrent Streaming, Viewer Transfers, and Audience Loyalty in a Creator Ecosystem: A Minute-Level Longitudinal Study

2026-03-26T08:52:18Z8b0202fbf5d1075d8bff27df819634ee1df91a8b39574199b53e466553bd600e
adversarial-mldetection-evasiondisinformationhuman-in-the-loophybrid-human-ailegal-riskmodel-hallucinationpolicy-implicationsprocurement-simulationreinforcement-learningsimulation-frameworksocial-botssupply-chain-manipulationverification-protocols

What happened

The document is an arXiv feed with several papers; the most security-relevant contributions are: (1) a controlled IRB-approved study of reinforcement-learning (RL)–powered adaptive social bots that dynamically evade detection, comparing human detectors, state-of-the-art AI detectors (including ML and LLMs), and hybrid human–AI ensembles. Key finding: hybrid aggregation of human reports with AI predictions (and retraining protocols using human supervision) outperforms humans alone and AI alone, with implications for defenses against adaptive covert social influence operations. (2) Analysis of a

Why it matters

A reviewed impact interpretation has not been published for this record.

Evidence and limitations

Source ID
arxiv_cs_si
Record identifier
8b0202fbf5d1075d8bff27df819634ee1df91a8b39574199b53e466553bd600e
Enrichment time
2026-03-26T08:52:18Z
AI-assisted enrichment
Yes

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