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.
Abstract (translated)
在线交流中的毒性内容检测仍是一项重大挑战，现有方案常会误拦包括医学术语和少数群体相关文本在内的有价值信息。本文提出了一种更精细的保加利亚语毒性识别方法，在检测毒性的同时保留关键信息的可访问性。本研究探索了两种截然不同的毒性内容检测方法，所开发的方法论可应用于各类在线平台和内容审核系统。首先，我们构建了一个对保加利亚语潜在毒性词汇进行建模的本体。随后，我们创建了一个包含4,384条人工标注语句的数据集，这些语句来自保加利亚在线论坛，涵盖四类：毒性语言、医学术语、非毒性语言及少数群体相关术语。接着，我们训练了一个基于BERT的毒性语言分类模型，其F1宏值达到0.89。该训练好的模型可直接应用于实际环境，并能作为毒性内容检测系统的组件进行集成。
URL
https://arxiv.org/abs/2604.01745
