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This paper addresses the challenge of toxic language detection in Bulgarian text by creating a manually annotated dataset of 4,384 sentences across four categories (toxic, medical, non-toxic, minority-related) and developing a BERT-based classification model. The ontology of potentially toxic words in Bulgarian language is used to inform the dataset creation. The resulting model achieves a 0.89 F1 macro score, demonstrating its potential for practical application in online content moderation.
A new Bulgarian toxicity detection dataset and BERT model pinpoint toxic language with high accuracy (0.89 F1), while avoiding the over-filtering of medical terms and minority-related discussions common in existing systems.
Toxic content detection in online communication remains a significant challenge, with current solutions often inadvertently blocking valuable information, including medical terms and text related to minority groups. This paper presents a more nu-anced approach to identifying toxicity in Bulgarian text while preserving access to essential information. The research explores two distinct methodologies for detecting toxic content. The developed methodologies have po-tential applications across diverse online platforms and content moderation systems. First, we propose an ontology that models the potentially toxic words in Bulgarian language. Then, we compose a dataset that comprises 4,384 manually anno-tated sentences from Bulgarian online forums across four categories: toxic language, medical terminology, non-toxic lan-guage, and terms related to minority communities. We then train a BERT-based model for toxic language classification, which reaches a 0.89 F1 macro score. The trained model is directly applicable in a real environment and can be integrated as a com-ponent of toxic content detection systems.