2026-07-20T14:44:42Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:14549 2024-03-06T10:31:25Z ddc:004 open_access

A Twitter Corpus and Lexicon for Abusive Speech Detection in Serbian Jokić, Danka Stanković, Ranka Krstev, Cvetana Šandrih, Branislava abusive language hate speech Serbian Twitter lexicon corpus Abusive speech in social media, including profanities, derogatory and hate speech, has reached the level of a pandemic. A system that would be able to detect such texts could help in making the Internet and social media a better and more respectful virtual space. Research and commercial application in this area were so far focused mainly on the English language. This paper presents the work on building AbCoSER, the first corpus of abusive speech in Serbian. The corpus consists of 6,436 manually annotated tweets, out of which 1,416 were labelled as tweets using some kind of abusive speech. Those 1,416 tweets were further sub-classified, for instance to those using vulgar, hate speech, derogatory language, etc. In this paper, we explain the process of data acquisition, annotation, and corpus construction. We also discuss the results of an initial analysis of the annotation quality. Finally, we present an abusive speech lexicon structure and its enrichment with abusive triggers extracted from the AbCoSER dataset. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Danka Jokić and Ranka Stanković and Cvetana Krstev and Branislava Šandrih 2021 Is Part Of OASIcs, Volume 93, 3rd Conference on Language, Data and Knowledge (LDK 2021) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.LDK.2021.13 urn:nbn:de:0030-drops-145493 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.LDK.2021.13 eng https://creativecommons.org/licenses/by/4.0/legalcode