From Bag-of-Words to Reasoning: Comparing Traditional Supervised ML and Zero-Shot LLMs for Sexual Predator Identification in Brazilian Portuguese - Santos, Leonardo Ferreira dos; Guedes, Gustavo (Anais do Brazilian Workshop on Social Network Analysis and Mining (BraSNAM), 2026)
Automated detection of online sexual predators has traditionally relied mostly on supervised classifiers using both textual and engineered features and annotation labels, simplifying the complexities of sexual predatory behavior. With the emergence of LLMs and the scarcity of real-world data, it is important to explore their potential in this research domain. In this work, four commercial LLMs with reasoning capabilities are evaluated in zero-shot mode on the PREDADORES-BR dataset for binary classification of predatory conversations in Brazilian Portuguese. The best-performing model achieved F 1 = 96% with 100% precision and zero false positives, with recall statistically indistinguishable from the best supervised baseline (SVM, F 1 = 89.87%).