Offensive Language Identification in Greek

TL;DR

This paper introduces the first Greek dataset for offensive language detection on Twitter, providing annotated data and evaluating various models to address the lack of Greek resources in this domain.

Contribution

It presents the OGTD, the first manually annotated Greek offensive language dataset, and evaluates multiple models on this new resource.

Findings

Models achieve varying accuracy levels on Greek offensive language detection

The dataset enables future research in Greek offensive language identification

Baseline results establish a benchmark for Greek offensive language detection

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.