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This paper introduces StoryScope, a pipeline that analyzes discourse-level narrative features to distinguish AI-generated fiction from human-written stories. It achieves high accuracy and reveals distinct narrative fingerprints for different LLMs like Claude, GPT, and Gemini.
A foundational study on applying stylometric authorship attribution to threat intelligence, using Japanese Rakuten reviews to compare TF-IDF+LR, BERT embedding, BERT fine-tuning, and metric learning methods. BERT-FT performed best overall, but TF-IDF+LR proved more stable and efficient when scaling to hundreds of authors.