Concurrent Streaming, Viewer Transfers, and Audience Loyalty in a Creator Ecosystem: A Minute-Level Longitudinal Study
2026-03-26T08:52:18Z•8b0202fbf5d1075d8bff27df819634ee1df91a8b39574199b53e466553bd600e
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
This record may overlap with other records. Its enrichment can be incomplete or wrong, and machine assistance was used. Validate consequential decisions against the linked source and your own environment.