Offensive Language Identification in Greek

Zesis Pitenis, Marcos Zampieri, Tharindu Ranasinghe


Abstract
As offensive language has become a rising issue for online communities and social media platforms, researchers have been investigating ways of coping with abusive content and developing systems to detect its different types: cyberbullying, hate speech, aggression, etc. With a few notable exceptions, most research on this topic so far has dealt with English. This is mostly due to the availability of language resources for English. To address this shortcoming, this paper presents the first Greek annotated dataset for offensive language identification: the Offensive Greek Tweet Dataset (OGTD). OGTD is a manually annotated dataset containing 4,779 posts from Twitter annotated as offensive and not offensive. Along with a detailed description of the dataset, we evaluate several computational models trained and tested on this data.
Anthology ID:
2020.lrec-1.629
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
5113–5119
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.629
DOI:
Bibkey:
Cite (ACL):
Zesis Pitenis, Marcos Zampieri, and Tharindu Ranasinghe. 2020. Offensive Language Identification in Greek. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 5113–5119, Marseille, France. European Language Resources Association.
Cite (Informal):
Offensive Language Identification in Greek (Pitenis et al., LREC 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.lrec-1.629.pdf
Code
 tharindudr/aggression-detection-greek
Data
OGTDOLID