  Online media aim for reaching ever bigger audience and for attracting ever
longer attention span. This competition creates an environment that rewards
sensational, fake, and toxic news. To help limit their spread and impact, we
propose and develop a news toxicity detector that can recognize various types
of toxic content. While previous research primarily focused on English, here we
target Bulgarian. We created a new dataset by crawling a website that for five
years has been collecting Bulgarian news articles that were manually
categorized into eight toxicity groups. Then we trained a multi-class
classifier with nine categories: eight toxic and one non-toxic. We experimented
with different representations based on ElMo, BERT, and XLM, as well as with a
variety of domain-specific features. Due to the small size of our dataset, we
created a separate model for each feature type, and we ultimately combined
these models into a meta-classifier. The evaluation results show an accuracy of
59.0% and a macro-F1 score of 39.7%, which represent sizable improvements over
the majority-class baseline (Acc=30.3%, macro-F1=5.2%).
