Generative AI and Digital Ecosystem Resilience: A Proactive Lifecycle-Based Survey 文章

ArXiv CS.CL2026-06-02NEWSen作者: Jonghyun Chung, Rishabh Chaddha, Sanket Badhe, Debanshu Das, Nathan Huang, Amanpreet Kaur

摘要

arXiv:2606.00136v1 Announce Type: cross Abstract: The proliferation of adversarial synthetic content, accelerated by Generative AI (GenAI) is rendering traditional reactive detection methods ineffective. This survey synthesizes emerging research to demonstrate a paradigm shift toward the proactive detection of emerging inauthentic narratives. In this survey, we adopt a unified, lifecycle-based taxonomy to combine socio-technical lifecycle models of adversarial campaigns with advanced computational methodologies for emerging inauthentic narrative detection. By structuring the analysis around the C5 Interaction Model (Context, Causes, Content, Cycle of Amplification, Consequences), we integrate different research streams from machine learning and social science.

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