Inappropriate Content Classification Model for Digital Violence Detection Using Hybrid Data

This study presents an inappropriate-content classification framework for digital-violence detection in Ecuadorian Spanish, addressing extreme class imbalance and dialectal variation. Data were collected in a post-API setting through a Selenium-based scraping pipeline that reconstructs conversational context using a window of ? = 3 prior interventions. An initial zero-shot labeling attempt with LLMs (Hermes) revealed severe cultural misinterpretation, overestimating the Violence class by 50 times (97.5% false alerts), which motivated full human validation and targeted data engineering. To correct imbalance without contaminating evaluation, the corpus was split before augmentation (70/15/15), and minority classes were selectively leveled via few-shot generation with LLaMA 3.1, followed by strict deduplication and cosine-similarity filtering (? = 0.85) to preserve semantic diversity. Model selection compared BETO and mBERT, with BETO outperforming. Across four training scenarios, naïve oversampling produced artificially inflated metrics indicative of overfitting, whereas the proposed cost-sensitive and regularized configuration (BETO with semantic deduplication and weighted loss) achieved 94.39% accuracy, 0.9429 weighted F1, and 0.9022 macro F1, significantly improving recovery of critical classes. Results highlight that hybrid data are effective only when carefully curated and paired with leakage-free evaluation protocols. This work demonstrates how machine learning innovation and knowledge extraction from heterogeneous data can be combined to build robust models for digital-violence detection in low-resource, culturally specific contexts.