Task Categories: text-classification
Languages: pl
Multilinguality: monolingual
Size Categories: 10K<n<100K
Licenses: unknown
Language Creators: found
Annotations Creators: found
Source Datasets: original

Dataset Card for Poleval 2019 cyberbullying

Dataset Summary

Task 6-1: Harmful vs non-harmful

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.

Task 6-2: Type of harmfulness

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).

Supported Tasks and Leaderboards

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Dataset Structure

Data Instances

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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)

Data Splits

Train and Test

Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

  editor    = {Maciej Ogrodniczuk and Łukasz Kobyliński},
  title     = {{Proceedings of the PolEval 2019 Workshop}},
  year      = {2019},
  address   = {Warsaw, Poland},
  publisher = {Institute of Computer Science, Polish Academy of Sciences},
  url       = {},
  isbn      = "978-83-63159-28-3"}


Thanks to @czabo for adding this dataset.

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