# Dataset: poleval2019_cyberbullying

Languages: pl
Multilinguality: monolingual
Size Categories: 10K<n<100K
Language Creators: found
Annotations Creators: found
Source Datasets: original

# Dataset Card for Poleval 2019 cyberbullying

### Dataset Summary

In this task, the participants are to distinguish between normal/non-harmful tweets (class: 0) and tweets that contain any kind of harmful information (class: 1). This includes cyberbullying, hate speech and related phenomena. The data for the task is available now and can be downloaded from the link provided below.

In this task, the participants shall distinguish between three classes of tweets: 0 (non-harmful), 1 (cyberbullying), 2 (hate-speech). There are various definitions of both cyberbullying and hate-speech, some of them even putting those two phenomena in the same group. The specific conditions on which we based our annotations for both cyberbullying and hate-speech, which have been worked out during ten years of research will be summarized in an introductory paper for the task, however, the main and definitive condition to distinguish the two is whether the harmful action is addressed towards a private person(s) (cyberbullying), or a public person/entity/large group (hate-speech).

Polish

## Dataset Structure

### Data Fields

• text: the provided tweet
• label: for task 6-1 the label can be 0 (non-harmful) or 1 (harmful)
   for task 6-2 the label can be 0 (non-harmful), 1 (cyberbullying) or 2 (hate-speech)

Train and Test

## Considerations for Using the Data

### Citation Information

@proceedings{ogr:kob:19:poleval,
editor    = {Maciej Ogrodniczuk and Łukasz Kobyliński},
title     = {{Proceedings of the PolEval 2019 Workshop}},
year      = {2019},
publisher = {Institute of Computer Science, Polish Academy of Sciences},
url       = {http://2019.poleval.pl/files/poleval2019.pdf},
isbn      = "978-83-63159-28-3"}
}

### Contributions

Thanks to @czabo for adding this dataset.

Homepage:
2019.poleval.pl
Paper:
Paper: