Download PDFOpen PDF in browserLightweight Authorship Attribution for Japanese Web Reviews: Character n-grams with Collective Attribution toward Actor Analysis12 pages•Published: August 6, 2026AbstractAs a foundational technique for actor analysis in threat intelligence, we study authorship attribution based on stylistic features. Practical post analysis must address text brevity, formulaic expressions, and growing author counts, motivating a cross-cutting comparison of lightweight feature-based methods and pre-trained language models across performance, stability, and computational cost. Using Rakuten Ichiba reviews, we compare 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) — under unified settings, scaling the number of authors U up to 1000.In our experiments, the lightweight TF-IDF+LR based on character n-grams achieves accuracy comparable to or higher than the BERT-based methods at U>=20, while requiring roughly 1/30 of BERT-FT's computation time at U=100 and 1/3 at U=1000. At U=1000 with n=186 reviews per author, TF-IDF+LR retains a Top-10 accuracy of 0.821, useful for candidate retrieval (screening). Applying Collective Attribution — a test-time aggregation in line with profile-based authorship attribution — to TF-IDF+LR, the concatenation variant improves accuracy at U=1000 from 0.623 with a single review (b=1) to 0.985 with ten reviews (b=10). For Japanese short web texts, our experiments suggest that combining lightweight character n-gram features with test-time aggregation offers a practical balance of accuracy, stability, and computational efficiency. Keyphrases: authorship attribution, character n grams, collective attribution, japanese web text, threat intelligence, top k evaluation In: Tung-Tso Tsai, Huy Kang Kim, Yujue Wang and Akira Yamada (editors). Proceedings of The 21st Asia Joint Conference on Information Security, vol 111, pages 111-122.
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