metadata

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"

Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Homepage:
Repository: https://figshare.com/articles/dataset/SHAJ_Albanian_hate_speech_abusive_language/19333298/1
Paper: https://arxiv.org/abs/2107.13592
Point of Contact: Leon Derczynski
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 for full details.

Supported Tasks and Leaderboards

Task A leaderboard at 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

Datasets:

strombergnlp
/

shaj