  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.
