What are the models tested on the dataset?
linear SVM, RBF SVM, linear classifier with SGDC, multinomial naive bayes, bernoulli naive bayes, pooled GRU, stacked LSTM with attention, LSTM and GRU with attention, 2d convolution with pooling, GRU with Capsule, LSTM with Capsule and attention, and BERT
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.
In the age of social media, offensive content online has become prevalent in recent years. There are many types of offensive content online such as racist and sexist posts and insults and threats targeted at individuals or groups. As such content increasingly occurs online, it has become a growing issue for online communities. This has come to the attention of social media platforms and authorities underlining the urgency to moderate and deal with such content. Several studies in NLP have approached offensive language identification applying machine learning and deep learning systems on annotated data to identify such content. Researchers in the field have worked with different definitions of offensive language with hate speech being the most studied among these types BIBREF0. BIBREF1 investigate the similarity between these sub-tasks. With a few noteworthy exceptions, most research so far has dealt with English, due to the availability of language resources. This gap in the literature recently started to be addressed with studies on Spanish BIBREF2, Hindi BIBREF3, and German BIBREF4, to name a few. In this paper we contribute in this direction presenting the first Greek annotated dataset for offensive language identification: the Offensive Greek Tweet Dataset (OGTD). OGTD uses a working definition of offensive language inspired by the OLID dataset for English BIBREF5 used in the recent OffensEval (SemEval-2019 Task 6) BIBREF6. In its version, 1.0 OGTD contains nearly 4,800 posts collected from Twitter and manually annotated by a team of volunteers, resulting in a high-quality annotated dataset. We trained a number of systems on this dataset and our best results have been obtained from a system using LSTMs and GRU with attention which achieved 0.89 F1 score.