Hate Speech in social media is a complex phenomenon, whose detection has recently
gained significant traction in the Natural Language Processing community, as attested by …

Detecting online toxicity has always been a challenge due to its inherent subjectivity. Factors
such as the context, geography, socio-political climate, and background of the producers …

We present the results and main findings of SemEval-2020 Task 12 on Multilingual
Offensive Language Identification in Social Media (OffensEval 2020). The task involves …

Nowadays, due to the great uncontrolled content posted daily on the Web, there has also
been a huge increase in the dissemination of hate speech worldwide. Social media, blogs …

This paper introduces a corpus of Turkish offensive language. To our knowledge, this is the
first corpus of offensive language for Turkish. The corpus consists of randomly sampled …

In this paper, we present the report and findings of the Shared Task on Aggression and
Gendered Aggression Identification organised as part of the Second Workshop on Trolling …

Abusive language detection is an emerging field in natural language processing which has
received a large amount of attention recently. Still the success of automatic detection is …

We present the GermEval 2021 shared task on the identification of toxic, engaging, and fact-
claiming comments. This shared task comprises three binary classification subtasks with the …

Ethnicity-targeted hate speech has been widely shown to influence on-the-ground inter-
ethnic conflict and violence, especially in such multi-ethnic societies as Russia. Therefore …

The availability of large annotated corpora from social media and the development of
powerful classification approaches have contributed in an unprecedented way to tackle the …