Expert-Annotated Dataset to Study Cyberbullying in Polish Language

Michal Ptaszynski (), Agata Pieciukiewicz, Pawel Dybala, Pawel Skrzek, Kamil Soliwoda, Marcin Fortuna, Gniewosz Leliwa and Michal Wroczynski
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Michal Ptaszynski: Text Information Processing Laboratory, Kitami Institute of Technology, Kitami 090-8507, Japan
Agata Pieciukiewicz: Polish-Japanese Academy of Information Technology, 02-008 Warszawa, Poland
Pawel Dybala: Institute of Middle and Far Eastern Studies, Faculty of International and Political Studies, Jagiellonian University, 30-059 Kraków, Poland
Pawel Skrzek: Samurai Labs, Aleja Zwyciȩstwa 96/98, 81-451 Gdynia, Poland
Kamil Soliwoda: Samurai Labs, Aleja Zwyciȩstwa 96/98, 81-451 Gdynia, Poland
Marcin Fortuna: Samurai Labs, Aleja Zwyciȩstwa 96/98, 81-451 Gdynia, Poland
Gniewosz Leliwa: Samurai Labs, Aleja Zwyciȩstwa 96/98, 81-451 Gdynia, Poland
Michal Wroczynski: Samurai Labs, Aleja Zwyciȩstwa 96/98, 81-451 Gdynia, Poland

Data, 2023, vol. 9, issue 1, 1-26

Abstract: We introduce the first dataset of harmful and offensive language collected from the Polish Internet. This dataset was meticulously curated to facilitate the exploration of harmful online phenomena such as cyberbullying and hate speech, which have exhibited a significant surge both within the Polish Internet as well as globally. The dataset was systematically collected and then annotated using two approaches. First, it was annotated by two proficient layperson volunteers, operating under the guidance of a specialist in the language of cyberbullying and hate speech. To enhance the precision of the annotations, a secondary round of annotations was carried out by a team of adept annotators with specialized long-term expertise in cyberbullying and hate speech annotations. This second phase was further overseen by an experienced annotator, acting as a super-annotator. In its initial application, the dataset was leveraged for the categorization of cyberbullying instances in the Polish language. Specifically, the dataset serves as the foundation for two distinct tasks: (1) a binary classification that segregates harmful and non-harmful messages and (2) a multi-class classification that distinguishes between two variations of harmful content (cyberbullying and hate speech), as well as a non-harmful category. Alongside the dataset itself, we also provide the models that showed satisfying classification performance. These models are made accessible for third-party use in constructing cyberbullying prevention systems.

Keywords: cyberbullying; hate speech; abusive language; offensive language; toxic language; automatic cyberbullying detection; polish language (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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