Offensive Language Identification in Greek
This addresses the problem of detecting offensive content for Greek online communities, but it is incremental as it applies existing methods to a new language.
The paper tackles the lack of offensive language identification resources for Greek by creating the first annotated dataset, OGTD, with 4,779 tweets, and evaluates computational models on it.
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.