Algorithmic Paranoia: Conflict over Digital Authenticity — Multimodal Dataset (v1.0)

This multimodal dataset documents a compact, high-intensity specimen of algorithmic paranoia, AI slop accusations, and identity destabilization emerging within a 48-hour YouTube comment-thread flashpoint surrounding creator Trent Slade. The collection captures how participants policed authenticity, accused both the creator and each other of being AI, and recursively escalated suspicion into a full memetic feedback loop. The dataset includes: Primary analytic PDF (AI_Slop_and_Algorithmic_Paranoia.pdf): A full case-study describing the incident, defining the core concepts AI Slop and Algorithmic Paranoia, and mapping the escalation engine driving the conflict. Discourse Capture PDF (ai_slop_discourse.pdf): The verbatim and annotated reconstruction of the comment-thread environment. ai_slop_discourse Images / Visual Assets (aislop.png, uft_ai_dynamics.png): Cultural-semiotic captures used for classification and visual reconstruction of the discourse field. NotebookLM Mind Map (NotebookLM_Mind_Map.png): A structural overview of the social-mechanical processes observed. Audio file (The_Algorithmic_Paranoia_Feedback_Loop_Explained.m4a): A spoken-analysis explainer detailing the emergent feedback loop model and key behavioral spirals. Together, these files form a structured micro-archive for research into: • digital authenticity policing• memetic conflict mechanics• identity destabilization in high-AI-saturation environments• computational linguistics under adversarial social pressure• the cultural evolution of “AI slop” as an epistemic category This dataset is intended as a reference specimen for sociologists, linguists, media theorists, and computational researchers studying the next phase of human–algorithm interaction dynamics.