PROPP: A Python library for narrative analysis

Harry Potter Character Interaction Networks: Analyzing character relationships, evolution, and narrative dynamics in J.K. Rowling's wizarding world using network science techniques and data from the Harry Potter series. Explore the magic of storytelling through data.

A pattern analysis report investigating how the “paperclip maximizer” AGI narrative distracts from real-time deployment of human-controlled surveillance systems. Includes documentation, funding trails, suppression tactics, and policy implications.

Statistical analysis investigating the relationship between information revelation patterns (measured via Kullback-Leibler divergence) and book popularity in English fiction. Features OLS and LASSO regression analysis, genre-specific modeling, and Project Gutenberg metadata analysis.

Expose Rhetorical Dodges: Prompt toolkit vs. performative complexity—refute claims that 'nuance' excuses evading models, stats, or core insights.

A Python implementation of hybrid Dynamic Topic Models and Large Language Models for detecting narrative shifts in longitudinal text corpora. Enables scalable analysis of media discourse evolution with statistical rigor and LLM-powered semantic interpretation.