Expert-Annotated Dataset to Study Cyberbullying in Polish Language
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...
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MDPI AG
2023-12-01
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| Online Access: | https://www.mdpi.com/2306-5729/9/1/1 |
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| author | Michal Ptaszynski Agata Pieciukiewicz Pawel Dybala Pawel Skrzek Kamil Soliwoda Marcin Fortuna Gniewosz Leliwa Michal Wroczynski |
| author_facet | Michal Ptaszynski Agata Pieciukiewicz Pawel Dybala Pawel Skrzek Kamil Soliwoda Marcin Fortuna Gniewosz Leliwa Michal Wroczynski |
| author_sort | Michal Ptaszynski |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T11:00:55Z |
| format | Article |
| id | doaj.art-30fdfca892b347fbb880f57a13ed1b5f |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-03-08T11:00:55Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-30fdfca892b347fbb880f57a13ed1b5f2024-01-26T15:59:15ZengMDPI AGData2306-57292023-12-0191110.3390/data9010001Expert-Annotated Dataset to Study Cyberbullying in Polish LanguageMichal Ptaszynski0Agata Pieciukiewicz1Pawel Dybala2Pawel Skrzek3Kamil Soliwoda4Marcin Fortuna5Gniewosz Leliwa6Michal Wroczynski7Text Information Processing Laboratory, Kitami Institute of Technology, Kitami 090-8507, JapanPolish-Japanese Academy of Information Technology, 02-008 Warszawa, PolandInstitute of Middle and Far Eastern Studies, Faculty of International and Political Studies, Jagiellonian University, 30-059 Kraków, PolandSamurai Labs, Aleja Zwyciȩstwa 96/98, 81-451 Gdynia, PolandSamurai Labs, Aleja Zwyciȩstwa 96/98, 81-451 Gdynia, PolandSamurai Labs, Aleja Zwyciȩstwa 96/98, 81-451 Gdynia, PolandSamurai Labs, Aleja Zwyciȩstwa 96/98, 81-451 Gdynia, PolandSamurai Labs, Aleja Zwyciȩstwa 96/98, 81-451 Gdynia, PolandWe 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.https://www.mdpi.com/2306-5729/9/1/1cyberbullyinghate speechabusive languageoffensive languagetoxic languageautomatic cyberbullying detection |
| spellingShingle | Michal Ptaszynski Agata Pieciukiewicz Pawel Dybala Pawel Skrzek Kamil Soliwoda Marcin Fortuna Gniewosz Leliwa Michal Wroczynski Expert-Annotated Dataset to Study Cyberbullying in Polish Language Data cyberbullying hate speech abusive language offensive language toxic language automatic cyberbullying detection |
| title | Expert-Annotated Dataset to Study Cyberbullying in Polish Language |
| title_full | Expert-Annotated Dataset to Study Cyberbullying in Polish Language |
| title_fullStr | Expert-Annotated Dataset to Study Cyberbullying in Polish Language |
| title_full_unstemmed | Expert-Annotated Dataset to Study Cyberbullying in Polish Language |
| title_short | Expert-Annotated Dataset to Study Cyberbullying in Polish Language |
| title_sort | expert annotated dataset to study cyberbullying in polish language |
| topic | cyberbullying hate speech abusive language offensive language toxic language automatic cyberbullying detection |
| url | https://www.mdpi.com/2306-5729/9/1/1 |
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