Foundational Study on Authorship Attribution of Japanese Web Reviews for Actor Analysis

arXiv cs.CL Papers
authorship-attribution nlp threat-intelligence bert stylometry japanese-nlp

Summary

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.

arXiv:2604.16376v1 Announce Type: new Abstract: This study investigates the applicability of authorship attribution based on stylistic features to support actor analysis in threat intelligence. As a foundational step toward future application to dark web forums, we conducted experiments using Japanese review data from clear web sources. We constructed datasets from Rakuten Ichiba reviews and compared four methods: TF-IDF with logistic regression (TF-IDF+LR), BERT embeddings with logistic regression (BERT-Emb+LR), BERT fine-tuning (BERT-FT), and metric learning with $k$-nearest neighbors (Metric+kNN). Results showed that BERT-FT achieved the best performance; however, training became unstable as the number of authors scaled to several hundred, where TF-IDF+LR proved superior in terms of accuracy, stability, and computational cost. Furthermore, Top-$k$ evaluation demonstrated the utility of candidate screening, and error analysis revealed that boilerplate text, topic dependency, and short text length were primary factors causing misclassification.
Original Article

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