Datasets:
Tasks:
Text Classification
Sub-tasks:
intent-classification
Languages:
Polish
Size:
10K<n<100K
License:
annotations_creators: | |
- found | |
language_creators: | |
- found | |
language: | |
- pl | |
license: | |
- unknown | |
multilinguality: | |
- monolingual | |
size_categories: | |
- 10K<n<100K | |
source_datasets: | |
- original | |
task_categories: | |
- text-classification | |
task_ids: | |
- intent-classification | |
pretty_name: Poleval 2019 cyberbullying | |
dataset_info: | |
- config_name: task01 | |
features: | |
- name: text | |
dtype: string | |
- name: label | |
dtype: | |
class_label: | |
names: | |
'0': '0' | |
'1': '1' | |
splits: | |
- name: train | |
num_bytes: 1104322 | |
num_examples: 10041 | |
- name: test | |
num_bytes: 109681 | |
num_examples: 1000 | |
download_size: 410001 | |
dataset_size: 1214003 | |
- config_name: task02 | |
features: | |
- name: text | |
dtype: string | |
- name: label | |
dtype: | |
class_label: | |
names: | |
'0': '0' | |
'1': '1' | |
'2': '2' | |
splits: | |
- name: train | |
num_bytes: 1104322 | |
num_examples: 10041 | |
- name: test | |
num_bytes: 109681 | |
num_examples: 1000 | |
download_size: 410147 | |
dataset_size: 1214003 | |
# Dataset Card for Poleval 2019 cyberbullying | |
## Table of Contents | |
- [Dataset Description](#dataset-description) | |
- [Dataset Summary](#dataset-summary) | |
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) | |
- [Languages](#languages) | |
- [Dataset Structure](#dataset-structure) | |
- [Data Instances](#data-instances) | |
- [Data Fields](#data-fields) | |
- [Data Splits](#data-splits) | |
- [Dataset Creation](#dataset-creation) | |
- [Curation Rationale](#curation-rationale) | |
- [Source Data](#source-data) | |
- [Annotations](#annotations) | |
- [Personal and Sensitive Information](#personal-and-sensitive-information) | |
- [Considerations for Using the Data](#considerations-for-using-the-data) | |
- [Social Impact of Dataset](#social-impact-of-dataset) | |
- [Discussion of Biases](#discussion-of-biases) | |
- [Other Known Limitations](#other-known-limitations) | |
- [Additional Information](#additional-information) | |
- [Dataset Curators](#dataset-curators) | |
- [Licensing Information](#licensing-information) | |
- [Citation Information](#citation-information) | |
- [Contributions](#contributions) | |
## Dataset Description | |
- **Homepage:** http://2019.poleval.pl/index.php/tasks/task6 | |
- **Repository:** | |
- **Paper:** | |
- **Leaderboard:** | |
- **Point of Contact:** | |
### 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 | |
[More Information Needed] | |
### Languages | |
Polish | |
## Dataset Structure | |
### Data Instances | |
[More Information Needed] | |
### 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 | |
[More Information Needed] | |
### Source Data | |
#### Initial Data Collection and Normalization | |
[More Information Needed] | |
#### Who are the source language producers? | |
[More Information Needed] | |
### Annotations | |
#### Annotation process | |
[More Information Needed] | |
#### Who are the annotators? | |
[More Information Needed] | |
### Personal and Sensitive Information | |
[More Information Needed] | |
## Considerations for Using the Data | |
### Social Impact of Dataset | |
[More Information Needed] | |
### Discussion of Biases | |
[More Information Needed] | |
### Other Known Limitations | |
[More Information Needed] | |
## Additional Information | |
### Dataset Curators | |
[More Information Needed] | |
### Licensing Information | |
[More Information Needed] | |
### Citation Information | |
``` | |
@proceedings{ogr:kob:19:poleval, | |
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 = {http://2019.poleval.pl/files/poleval2019.pdf}, | |
isbn = "978-83-63159-28-3"} | |
} | |
``` | |
### Contributions | |
Thanks to [@czabo](https://github.com/czabo) for adding this dataset. |