Abstract
The ever growing usage of social media in the recent years has had a direct impact on the increased presence of hate speech and offensive speech in online platforms. Research on effective detection of such content has mainly focused on English and a few other widespread languages, while the leftover majority fail to have the same work put into them and thus cannot benefit from the steady advancements made in the field. In this paper we present SHAJ, an annotated Albanian dataset for hate speech and offensive speech that has been constructed from user-generated content on various social media platforms. Its annotation follows the hierarchical schema introduced in OffensEval. The dataset is tested using three different classification models, the best of which achieves an F1 score of 0.77 for the identification of offensive language, 0.64 F1 score for the automatic categorization of offensive types and lastly, 0.52 F1 score for the offensive language target identification.
Erida Nurce
Microsoft
Erida worked on NLP approaches to multilingual abusive language detection and zero-shot cross-lingual projection.
Jorgel Keci
UN Development Program
Jorgel worked on NLP approaches to multilingual abusive language detection and zero-shot cross-lingual projection.
Leon Derczynski
Associate professor
My research interests include NLP for misinformation detection and verification, clinical record processing, online harms, and efficient AI.