README.md

---
annotations_creators:
- expert-generated
language_creators:
- found
languages:
- sq
- sq-AL
licenses:
- cc-by-4.0
multilinguality:
- monolingual
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: shaj
pretty_name: SHAJ
extra_gated_prompt: "Warning: this repository contains harmful content (abusive language, hate speech)."
---

# Dataset Card for "shaj"

## 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:** 
- **Repository:** [https://figshare.com/articles/dataset/SHAJ_Albanian_hate_speech_abusive_language/19333298/1](https://figshare.com/articles/dataset/SHAJ_Albanian_hate_speech_abusive_language/19333298/1)
- **Paper:** [https://arxiv.org/abs/2107.13592](https://arxiv.org/abs/2107.13592)
- **Point of Contact:** [Leon Derczynski](https://github.com/leondz)
- **Size of downloaded dataset files:** 769.21 KiB
- **Size of the generated dataset:** 1.06 MiB
- **Total amount of disk used:**  1.85 MiB

### Dataset Summary

This is an abusive/offensive language detection dataset for Albanian. The data is formatted
following the OffensEval convention, with three tasks:

* Subtask A: Offensive (OFF) or not (NOT)
* Subtask B: Untargeted (UNT) or targeted insult (TIN)
* Subtask C: Type of target: individual (IND), group (GRP), or other (OTH)

Notes on the above:

* The subtask A field should always be filled.
* The subtask B field should only be filled if there's "offensive" (OFF) in A.
* The subtask C field should only be filled if there's "targeted" (TIN) in B.

The dataset name is a backronym, also standing for "Spoken Hate in the Albanian Jargon"

See the paper [https://arxiv.org/abs/2107.13592](https://arxiv.org/abs/2107.13592) for full details.

### Supported Tasks and Leaderboards

* Task A leaderboard at [paperswithcode.com/sota/hate-speech-detection-on-shaj](https://paperswithcode.com/sota/hate-speech-detection-on-shaj)

### Languages

Albanian (`bcp47:sq-AL`)

## Dataset Structure

### Data Instances

#### shaj

- **Size of downloaded dataset files:** 769.21 KiB
- **Size of the generated dataset:** 1.06 MiB
- **Total amount of disk used:**  1.85 MiB

An example of 'train' looks as follows.

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


### Data Fields

- `id`: a `string` feature.
- `text`: a `string`.
- `subtask_a`: whether or not the instance is offensive; `0: OFF, 1: NOT`
- `subtask_b`: whether an offensive instance is a targeted insult; `0: TIN, 1: UNT, 2: not applicable`
- `subtask_c`: what a targeted insult is aimed at; `0: IND, 1: GRP, 2: OTH, 3: not applicable`


### Data Splits

|  name   |train|
|---------|----:|
|shaj|11874 sentences|

## Dataset Creation

### Curation Rationale

Collecting data for enabling offensive speech detection in Albanian

### Source Data

#### Initial Data Collection and Normalization

The text is scraped from comments on popular Albanian YouTube and Instagram accounts.
An extended discussion is given in the paper in section 3.2.

#### Who are the source language producers?

People who comment on a selection of high-activity Albanian instagram and youtube profiles.

### Annotations

#### Annotation process

The annotation scheme was taken from OffensEval 2019 and applied by two native speaker authors of the paper as well as their friends and family.

#### Who are the annotators?

Albanian native speakers, male and female, aged 20-60.

### 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.

### Discussion of Biases


### Other Known Limitations


## Additional Information

### Dataset Curators

The dataset is curated by the paper's authors.

### Licensing Information

The authors distribute this data under Creative Commons attribution license, CC-BY 4.0. 

### Citation Information

```
@article{nurce2021detecting,
  title={Detecting Abusive Albanian},
  author={Nurce, Erida and Keci, Jorgel and Derczynski, Leon},
  journal={arXiv preprint arXiv:2107.13592},
  year={2021}
}
```


### Contributions

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