Research on Lightweight Stylistic Features for Author Verification

Abstract

Author verification is a fundamental task in natural language processing with potential applications in digital forensics, copyright dispute resolution, and social media account linkage. While existing deep pre-trained models achieve high accuracy, they incur substantial computational costs, high inference latency, and poor interpretability, limiting their deployment in resource-constrained environments. To address this gap, a lightweight and interpretable author verification method is proposed that relies solely on hand-crafted stylistic features and classical machine learning classifiers, requiring no GPU acceleration. Specifically, three complementary feature sets are extracted per text: 37 punctuation features, 22 text-style features, and an 800-dimensional character-level TF-IDF vector. For each text pair, we compute the absolute difference and element-wise product for each feature group separately, and we also concatenate all resulting vectors to form the final classification representation. On a public review dataset under a strict author-level split, the proposed method achieves competitive verification performance with significantly improved training and inference efficiency. Notably, while the lightweight neural architectures evaluated in this study yield marginal accuracy gains, they incur substantially longer inference and training times, validating the efficiency-performance trade-off of our approach. Ablation studies confirm the contribution of each feature group, with text statistics being the most influential. The proposed method offers an efficient, transparent, and easily deployable solution for author verification in resource-limited settings and provides empirical evidence for integrating traditional stylistic features with lightweight models.