
# Dataset Card for "offenseval_2020"

## 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:** [https://sites.google.com/site/offensevalsharedtask/results-and-paper-submission](https://sites.google.com/site/offensevalsharedtask/results-and-paper-submission)
- **Repository:** 
- **Paper:** [https://aclanthology.org/2020.semeval-1.188/](https://aclanthology.org/2020.semeval-1.188/), [https://arxiv.org/abs/2006.07235](https://arxiv.org/abs/2006.07235)
- **Point of Contact:** [Leon Derczynski](https://github.com/leondz)

### Dataset Summary

OffensEval 2020 features a multilingual dataset with five languages. The languages included in OffensEval 2020 are:

* Arabic
* Danish
* English
* Greek
* Turkish

The annotation follows the hierarchical tagset proposed in the Offensive Language Identification Dataset (OLID) and used in OffensEval 2019. 
In this taxonomy we break down offensive content into the following three sub-tasks taking the type and target of offensive content into account. 
The following sub-tasks were organized:

* Sub-task A - Offensive language identification;
* Sub-task B - Automatic categorization of offense types;
* Sub-task C - Offense target identification.

English training data is omitted so needs to be collected otherwise (see [https://zenodo.org/record/3950379#.XxZ-aFVKipp](https://zenodo.org/record/3950379#.XxZ-aFVKipp))

The source datasets come from:

* Arabic [https://arxiv.org/pdf/2004.02192.pdf](https://arxiv.org/pdf/2004.02192.pdf), [https://aclanthology.org/2021.wanlp-1.13/](https://aclanthology.org/2021.wanlp-1.13/)
* Danish [https://arxiv.org/pdf/1908.04531.pdf](https://arxiv.org/pdf/1908.04531.pdf), [https://aclanthology.org/2020.lrec-1.430/?ref=https://githubhelp.com](https://aclanthology.org/2020.lrec-1.430/)
* English [https://arxiv.org/pdf/2004.14454.pdf](https://arxiv.org/pdf/2004.14454.pdf), [https://aclanthology.org/2021.findings-acl.80.pdf](https://aclanthology.org/2021.findings-acl.80.pdf)
* Greek [https://arxiv.org/pdf/2003.07459.pdf](https://arxiv.org/pdf/2003.07459.pdf), [https://aclanthology.org/2020.lrec-1.629/](https://aclanthology.org/2020.lrec-1.629/)
* Turkish [https://aclanthology.org/2020.lrec-1.758/](https://aclanthology.org/2020.lrec-1.758/)

### Supported Tasks and Leaderboards

* [OffensEval 2020](https://sites.google.com/site/offensevalsharedtask/results-and-paper-submission)

### Languages

Five are covered: bcp47 `ar;da;en;gr;tr`

## Dataset Structure

There are five named configs, one per language:

* `ar` Arabic
* `da` Danish
* `en` English
* `gr` Greek
* `tr` Turkish

The training data for English is absent - this is 9M tweets that need to be rehydrated on their own. See [https://zenodo.org/record/3950379#.XxZ-aFVKipp](https://zenodo.org/record/3950379#.XxZ-aFVKipp)

### Data Instances


An example of 'train' looks as follows.

```
{
  'id': '0', 
  'text': 'PLACEHOLDER TEXT', 
  'subtask_a': 1, 
}
```


### Data Fields

- `id`: a `string` feature.
- `text`: a `string`.
- `subtask_a`: whether or not the instance is offensive; `0: NOT, 1: OFF`


### Data Splits

|  name   |train|test|
|---------|----:|---:|
|ar|7839|1827|
|da|2961|329|
|en|0|3887|
|gr|8743|1544|
|tr|31277|3515|

## Dataset Creation

### Curation Rationale

Collecting data for abusive language classification. Different rational for each dataset.

### Source Data

#### Initial Data Collection and Normalization

Varies per language dataset

#### Who are the source language producers?

Social media users

### Annotations

#### Annotation process

Varies per language dataset

#### Who are the annotators?

Varies per language dataset; native speakers

### Personal and Sensitive Information

The data was public at the time of collection. No PII removal has been performed.

## Considerations for Using the Data

### Social Impact of Dataset

The data definitely contains abusive language. The data could be used to develop and propagate offensive language against every target group involved, i.e. ableism, racism, sexism, ageism, and so on.

### Discussion of Biases


### Other Known Limitations


## Additional Information

### Dataset Curators

The datasets is curated by each sub-part's paper authors.

### Licensing Information

This data is available and distributed under Creative Commons attribution license, CC-BY 4.0. 

### Citation Information

```
@inproceedings{zampieri-etal-2020-semeval,
    title = "{S}em{E}val-2020 Task 12: Multilingual Offensive Language Identification in Social Media ({O}ffens{E}val 2020)",
    author = {Zampieri, Marcos  and
      Nakov, Preslav  and
      Rosenthal, Sara  and
      Atanasova, Pepa  and
      Karadzhov, Georgi  and
      Mubarak, Hamdy  and
      Derczynski, Leon  and
      Pitenis, Zeses  and
      {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://aclanthology.org/2020.semeval-1.188",
    doi = "10.18653/v1/2020.semeval-1.188",
    pages = "1425--1447",
    abstract = "We present the results and the main findings of SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval-2020). The task included three subtasks corresponding to the hierarchical taxonomy of the OLID schema from OffensEval-2019, and it was offered in five languages: Arabic, Danish, English, Greek, and Turkish. OffensEval-2020 was one of the most popular tasks at SemEval-2020, attracting a large number of participants across all subtasks and languages: a total of 528 teams signed up to participate in the task, 145 teams submitted official runs on the test data, and 70 teams submitted system description papers.",
}

```


### Contributions

Author-added dataset [@leondz](https://github.com/leondz) 

task_categories:text-classification task_ids:hate-speech-detection annotations_creators:expert-generated language_creators:found multilinguality:multilingual size_categories:10K<n<100K source_datasets:original arxiv:2006.07235 arxiv:2004.02192 arxiv:1908.04531 arxiv:2004.14454 arxiv:2003.07459 region:us
{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["hate-speech-detection", "text-classification-other-hate-speech-detection"], "paperswithcode_id": ["dkhate", "ogtd"], "pretty_name": "OffensEval 2020", "languages": ["ar", "da", "en", "gr", "tr"], "licenses": ["cc-by-4.0"], "extra_gated_prompt": "Warning: this repository contains harmful content (abusive language, hate speech)."}
TAGS
#task_categories-text-classification #task_ids-hate-speech-detection #annotations_creators-expert-generated #language_creators-found #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #arxiv-2006.07235 #arxiv-2004.02192 #arxiv-1908.04531 #arxiv-2004.14454 #arxiv-2003.07459 #region-us 

Dataset Card for "offenseval\_2020"
===================================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information
	+ Contributions


Dataset Description
-------------------


* Homepage: URL
* Repository:
* Paper: URL URL
* Point of Contact: Leon Derczynski


### Dataset Summary


OffensEval 2020 features a multilingual dataset with five languages. The languages included in OffensEval 2020 are:


* Arabic
* Danish
* English
* Greek
* Turkish


The annotation follows the hierarchical tagset proposed in the Offensive Language Identification Dataset (OLID) and used in OffensEval 2019.
In this taxonomy we break down offensive content into the following three sub-tasks taking the type and target of offensive content into account.
The following sub-tasks were organized:


* Sub-task A - Offensive language identification;
* Sub-task B - Automatic categorization of offense types;
* Sub-task C - Offense target identification.


English training data is omitted so needs to be collected otherwise (see URL


The source datasets come from:


* Arabic URL URL
* Danish URL URL/URL
* English URL URL
* Greek URL URL
* Turkish URL


### Supported Tasks and Leaderboards


* OffensEval 2020


### Languages


Five are covered: bcp47 'ar;da;en;gr;tr'


Dataset Structure
-----------------


There are five named configs, one per language:


* 'ar' Arabic
* 'da' Danish
* 'en' English
* 'gr' Greek
* 'tr' Turkish


The training data for English is absent - this is 9M tweets that need to be rehydrated on their own. See URL


### Data Instances


An example of 'train' looks as follows.


### Data Fields


* 'id': a 'string' feature.
* 'text': a 'string'.
* 'subtask\_a': whether or not the instance is offensive; '0: NOT, 1: OFF'


### Data Splits



Dataset Creation
----------------


### Curation Rationale


Collecting data for abusive language classification. Different rational for each dataset.


### Source Data


#### Initial Data Collection and Normalization


Varies per language dataset


#### Who are the source language producers?


Social media users


### Annotations


#### Annotation process


Varies per language dataset


#### Who are the annotators?


Varies per language dataset; native speakers


### Personal and Sensitive Information


The data was public at the time of collection. No PII removal has been performed.


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


The data definitely contains abusive language. The data could be used to develop and propagate offensive language against every target group involved, i.e. ableism, racism, sexism, ageism, and so on.


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


The datasets is curated by each sub-part's paper authors.


### Licensing Information


This data is available and distributed under Creative Commons attribution license, CC-BY 4.0.


### Contributions


Author-added dataset @leondz



### Dataset Summary


OffensEval 2020 features a multilingual dataset with five languages. The languages included in OffensEval 2020 are:


* Arabic
* Danish
* English
* Greek
* Turkish


The annotation follows the hierarchical tagset proposed in the Offensive Language Identification Dataset (OLID) and used in OffensEval 2019.
In this taxonomy we break down offensive content into the following three sub-tasks taking the type and target of offensive content into account.
The following sub-tasks were organized:


* Sub-task A - Offensive language identification;
* Sub-task B - Automatic categorization of offense types;
* Sub-task C - Offense target identification.


English training data is omitted so needs to be collected otherwise (see URL


The source datasets come from:


* Arabic URL URL
* Danish URL URL/URL
* English URL URL
* Greek URL URL
* Turkish URL ### Supported Tasks and Leaderboards


* OffensEval 2020 ### Languages


Five are covered: bcp47 'ar;da;en;gr;tr'


Dataset Structure
-----------------


There are five named configs, one per language:


* 'ar' Arabic
* 'da' Danish
* 'en' English
* 'gr' Greek
* 'tr' Turkish


The training data for English is absent - this is 9M tweets that need to be rehydrated on their own. See URL ### Data Instances


An example of 'train' looks as follows. ### Data Fields


* 'id': a 'string' feature.
* 'text': a 'string'.
* 'subtask\_a': whether or not the instance is offensive; '0: NOT, 1: OFF' ### Data Splits



Dataset Creation
---------------- ### Curation Rationale


Collecting data for abusive language classification. Different rational for each dataset. ### Source Data #### Initial Data Collection and Normalization


Varies per language dataset #### Who are the source language producers?


Social media users ### Annotations #### Annotation process


Varies per language dataset #### Who are the annotators?


Varies per language dataset; native speakers ### Personal and Sensitive Information


The data was public at the time of collection. No PII removal has been performed.


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset


The data definitely contains abusive language. The data could be used to develop and propagate offensive language against every target group involved, i.e. ableism, racism, sexism, ageism, and so on. ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators


The datasets is curated by each sub-part's paper authors. ### Licensing Information


This data is available and distributed under Creative Commons attribution license, CC-BY 4.0. ### Contributions


Author-added dataset @leondz
TAGS
#task_categories-text-classification #task_ids-hate-speech-detection #annotations_creators-expert-generated #language_creators-found #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #arxiv-2006.07235 #arxiv-2004.02192 #arxiv-1908.04531 #arxiv-2004.14454 #arxiv-2003.07459 #region-us 
 ### Dataset Summary


OffensEval 2020 features a multilingual dataset with five languages. The languages included in OffensEval 2020 are:


* Arabic
* Danish
* English
* Greek
* Turkish


The annotation follows the hierarchical tagset proposed in the Offensive Language Identification Dataset (OLID) and used in OffensEval 2019.
In this taxonomy we break down offensive content into the following three sub-tasks taking the type and target of offensive content into account.
The following sub-tasks were organized:


* Sub-task A - Offensive language identification;
* Sub-task B - Automatic categorization of offense types;
* Sub-task C - Offense target identification.


English training data is omitted so needs to be collected otherwise (see URL


The source datasets come from:


* Arabic URL URL
* Danish URL URL/URL
* English URL URL
* Greek URL URL
* Turkish URL ### Supported Tasks and Leaderboards


* OffensEval 2020 ### Languages


Five are covered: bcp47 'ar;da;en;gr;tr'


Dataset Structure
-----------------


There are five named configs, one per language:


* 'ar' Arabic
* 'da' Danish
* 'en' English
* 'gr' Greek
* 'tr' Turkish


The training data for English is absent - this is 9M tweets that need to be rehydrated on their own. See URL ### Data Instances


An example of 'train' looks as follows. ### Data Fields


* 'id': a 'string' feature.
* 'text': a 'string'.
* 'subtask\_a': whether or not the instance is offensive; '0: NOT, 1: OFF' ### Data Splits



Dataset Creation
---------------- ### Curation Rationale


Collecting data for abusive language classification. Different rational for each dataset. ### Source Data #### Initial Data Collection and Normalization


Varies per language dataset #### Who are the source language producers?


Social media users ### Annotations #### Annotation process


Varies per language dataset #### Who are the annotators?


Varies per language dataset; native speakers ### Personal and Sensitive Information


The data was public at the time of collection. No PII removal has been performed.


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset


The data definitely contains abusive language. The data could be used to develop and propagate offensive language against every target group involved, i.e. ableism, racism, sexism, ageism, and so on. ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators


The datasets is curated by each sub-part's paper authors. ### Licensing Information


This data is available and distributed under Creative Commons attribution license, CC-BY 4.0. ### Contributions


Author-added dataset @leondz
passage: TAGS
#task_categories-text-classification #task_ids-hate-speech-detection #annotations_creators-expert-generated #language_creators-found #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #arxiv-2006.07235 #arxiv-2004.02192 #arxiv-1908.04531 #arxiv-2004.14454 #arxiv-2003.07459 #region-us 
### Dataset Summary


OffensEval 2020 features a multilingual dataset with five languages. The languages included in OffensEval 2020 are:


* Arabic
* Danish
* English
* Greek
* Turkish


The annotation follows the hierarchical tagset proposed in the Offensive Language Identification Dataset (OLID) and used in OffensEval 2019.
In this taxonomy we break down offensive content into the following three sub-tasks taking the type and target of offensive content into account.
The following sub-tasks were organized:


* Sub-task A - Offensive language identification;
* Sub-task B - Automatic categorization of offense types;
* Sub-task C - Offense target identification.


English training data is omitted so needs to be collected otherwise (see URL


The source datasets come from:


* Arabic URL URL
* Danish URL URL/URL
* English URL URL
* Greek URL URL
* Turkish URL### Supported Tasks and Leaderboards


* OffensEval 2020### Languages


Five are covered: bcp47 'ar;da;en;gr;tr'


Dataset Structure
-----------------


There are five named configs, one per language:


* 'ar' Arabic
* 'da' Danish
* 'en' English
* 'gr' Greek
* 'tr' Turkish


The training data for English is absent - this is 9M tweets that need to be rehydrated on their own. See URL### Data Instances


An example of 'train' looks as follows.