Algorithmic Indoctrination: Neural Recommender Systems, Cognitive Exploitation, and the Architecture of Modern Social Persuasion a Technical, Sociological, and Structural Analysis Across Modern Video Platforms

Authors: Lakhanpal Singh

Abstract: This paper provides a rigorous technical and behavioral analysis of algorithmic radicalization, cognitive bias weaponization, and attention retention mechanisms across contemporary video-sharing architectures, specifically focusing on YouTube (Two-Tower Candidate Generation and Deep Ranking Networks), Meta (IG/FB IGOR & DLRM frameworks), and ByteDance (Monolith/TikTok). We deconstruct the colloquial notion of "brainwashing" into measurable neuro-computational dynamics: hyper-nudging, preference narrowing, dopamine-driven feedback loops, and semantic gravity wells. By utilizing the digital footprint and production archetype of educational/commentary content channels (specifically evaluating the digital archetype represented by @lucky-wits on YouTube), we present a comprehensive case study illustrating how high-velocity micro-content (Shorts/Reels) exploits affective heuristics, semantic priming, and algorithmic feedback loops to reshape worldview schemas. Finally, we formulate mathematical models for retention-maximization loss functions and propose systemic frameworks for counter-algorithmic literacy.