Building Target-Language Cybergrooming Detectors from English-Language Datasets: A Machine Translation Pipeline

Abstract

Automated cybergrooming detection can support the review of large volumes of online communication, but public datasets and detection approaches remain predominantly English-centered. This study presents a reproducible workflow for constructing target-language cybergrooming datasets and detection models from English resources, demonstrated for German. To the best of our knowledge, this is the first published study to construct and systematically evaluate German-language cybergrooming datasets and detection models. Four publicly available English cybergrooming datasets were converted into a shared structure and translated into informal German chat language. Translation quality was estimated using an aggregate of COMETKiwi and MetricX scores termed CometricX. Low-quality messages were retranslated and reevaluated. Cybergrooming messages were additionally augmented through German–Catalan backtranslation. The resulting datasets, with and without augmentation, were used to train and evaluate embedding-based linear support vector machines (SVMs) and ModernBERT models. Evaluation comprised translated test data and 156 manually labeled German conversations from five closed law-enforcement cases. The Perverted Justice Zig ChitChat dataset (PJZC) achieved the highest aggregate quality estimate and the most robust detection performance on real-world German conversations. In contrast, performance on machine-translated test data overestimated real-world detection performance. Furthermore, backtranslation yielded only limited and inconsistent improvements. Overall, the proposed workflow provides a practical starting point for developing target-language cybergrooming detectors while highlighting careful source dataset selection and evaluation on authentic target-language data.