Title: A Corpus of Turkish Offensive Language on Social Media
Abstract: https://aclanthology.org/2020.lrec-1.758/
This paper introduces a corpus of Turkish offensive language. To our knowledge, this is the first corpus of offensive language for Turkish. The corpus consists of randomly sampled micro-blog posts from Twitter. The annotation guidelines are based on a careful review of the annotation practices of recent efforts for other languages. The corpus contains 36 232 tweets sampled randomly from the Twitter stream during a period of 18 months between Apr 2018 to Sept 2019. We found approximately 19 % of the tweets in the data contain some type of offensive language, which is further subcategorized based on the target of the offense. We describe the annotation process, discuss some interesting aspects of the data, and present results of automatically classifying the corpus using state-of-the-art text classification methods. The classifiers achieve 77.3 % F1 score on identifying offensive tweets, 77.9 % F1 score on determining whether a given offensive document is targeted or not, and 53.0 % F1 score on classifying the targeted offensive documents into three subcategories.
Homepage: https://huggingface.co/datasets/offenseval2020_tr
@inproceedings{coltekin-2020-corpus,
title = "A Corpus of {T}urkish Offensive Language on Social Media",
author = {{\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.758",
pages = "6174--6184",
abstract = "This paper introduces a corpus of Turkish offensive language. To our knowledge, this is the first corpus of offensive language for Turkish. The corpus consists of randomly sampled micro-blog posts from Twitter. The annotation guidelines are based on a careful review of the annotation practices of recent efforts for other languages. The corpus contains 36 232 tweets sampled randomly from the Twitter stream during a period of 18 months between Apr 2018 to Sept 2019. We found approximately 19 {\%} of the tweets in the data contain some type of offensive language, which is further subcategorized based on the target of the offense. We describe the annotation process, discuss some interesting aspects of the data, and present results of automatically classifying the corpus using state-of-the-art text classification methods. The classifiers achieve 77.3 {\%} F1 score on identifying offensive tweets, 77.9 {\%} F1 score on determining whether a given offensive document is targeted or not, and 53.0 {\%} F1 score on classifying the targeted offensive documents into three subcategories.",
language = "English",
ISBN = "979-10-95546-34-4",
}