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A Twitter Corpus and Lexicon for Abusive Speech Detection in Serbian

Authors Danka Jokić , Ranka Stanković , Cvetana Krstev , Branislava Šandrih

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Author Details

Danka Jokić
  • University of Belgrade, Serbia
Ranka Stanković
  • Faculty of Mining and Geology, University of Belgrade, Serbia
Cvetana Krstev
  • Faculty of Philology, University of Belgrade, Serbia
Branislava Šandrih
  • Faculty of Philology, University of Belgrade, Serbia

Funding

Linked data development is supported by the COST Action CA18209-NexusLinguarum "European network for Web-centred linguistic data science". Access to SketchEngine and Lexonomy is provided by the ELEXIS project funded by the European Union’s Horizon 2020 research and innovation program under grant number 731015.
  • Šandrih, Branislava: COST Action CA18209-NexusLinguarum "European network for Web-centred linguistic data science"

Acknowledgements

We would like to acknowledge the team of annotators that provided their time and efforts to help us build AbCoSER v1.0 corpus of abusive speech in Serbian.

Cite As Get BibTex

Danka Jokić, Ranka Stanković, Cvetana Krstev, and Branislava Šandrih. A Twitter Corpus and Lexicon for Abusive Speech Detection in Serbian. In 3rd Conference on Language, Data and Knowledge (LDK 2021). Open Access Series in Informatics (OASIcs), Volume 93, pp. 13:1-13:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/OASIcs.LDK.2021.13

Abstract

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.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Natural language processing
Keywords
  • abusive language
  • hate speech
  • Serbian
  • Twitter
  • lexicon
  • corpus

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