Detecting Toxic Language: Ontology and BERT-based Approaches for Bulgarian Text

• URL: http://arxiv.org/abs/2604.01745v1
• Date: Thu, 02 Apr 2026 08:06:26 GMT
• Title: Detecting Toxic Language: Ontology and BERT-based Approaches for Bulgarian Text
• Abstract summary: This paper presents a more nu-anced approach to identifying toxicity in Bulgarian text while preserving access to essential information.<n>The developed methodologies have po-tential applications across diverse online platforms and content moderation systems.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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