Datasets:
annotations_creators:
- found
language_creators:
- found
languages:
- tr
licenses:
- found
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-classification-other-offensive-language-classification
Dataset Card for OffensEval-TR 2020
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: offensive-turkish
- Paper: A Corpus of Turkish Offensive Language on Social Media
- Point of Contact: Çağrı Çöltekin
Dataset Summary
The file offenseval-tr-training-v1.tsv contains 31,756 annotated tweets.
The file offenseval-annotation.txt contains a short summary of the annotation guidelines.
Twitter user mentions were substituted by @USER and URLs have been substitute by URL.
Each instance contains up to 1 labels corresponding to one of the following sub-task:
- Sub-task A: Offensive language identification;
Supported Tasks and Leaderboards
The dataset was published on this paper.
Languages
The dataset is based on Turkish.
Dataset Structure
Data Instances
A binary dataset with with (NOT) Not Offensive and (OFF) Offensive tweets.
Data Fields
Instances are included in TSV format as follows:
ID INSTANCE SUBA
The column names in the file are the following:
id tweet subtask_a
The labels used in the annotation are listed below.
Task and Labels
(A) Sub-task A: Offensive language identification
- (NOT) Not Offensive - This post does not contain offense or profanity.
- (OFF) Offensive - This post contains offensive language or a targeted (veiled or direct) offense
In our annotation, we label a post as offensive (OFF) if it contains any form of non-acceptable language (profanity) or a targeted offense, which can be veiled or direct.
Data Splits
| Tain | Test |
|---|---|
| 31756 | 3528 |
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
[More Information Needed]
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
From tweeter.
Annotations
[More Information Needed]
Annotation process
We describe the labels above in a “flat” manner. However, the annotation process we follow is hierarchical. The following QA pairs give a more flowchart-like procedure to follow
- Is the tweet in Turkish and understandable?
- No: mark tweet X for exclusion, and go to next tweet
- Yes: continue to step 2
- Is the tweet include offensive/inappropriate language?
- No: mark the tweet non go to step 4
- Yes: continue to step 3
- Is the offense in the tweet targeted?
- No: mark the tweet prof go to step 4
- Yes: chose one (or more) of grp, ind, *oth based on the definitions above. Please try to limit the number of labels unless it is clear that the tweet includes offense against multiple categories.
- Was the labeling decision difficult (precise answer needs more context, tweets includes irony, or for another reason)?
- No: go to next tweet
- Yes: add the label X, go to next tweet
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Discussion of Social Impact and Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
The annotations are distributed under the terms of Creative Commons Attribution License (CC-BY). Please cite the following paper, if you use this resource.
Citation Information
@inproceedings{coltekin2020lrec,
author = {\c{C}\"{o}ltekin, \c{C}a\u{g}r{\i}},
year = {2020},
title = {A Corpus of Turkish Offensive Language on Social Media},
booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference},
pages = {6174--6184},
address = {Marseille, France},
url = {https://www.aclweb.org/anthology/2020.lrec-1.758},
}
Contributions
Thanks to @yavuzKomecoglu for adding this dataset.