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 …

Online toxic discourses could result in conflicts between groups or harm to online
communities. Hate speech is complex and multifaceted harmful or offensive content …

In this paper, we introduce HateBERT, a re-trained BERT model for abusive language
detection in English. The model was trained on RAL-E, a large-scale dataset of Reddit …

Hate speech has grown significantly on social media, causing serious consequences for
victims of all demographics. Despite much attention being paid to characterize and detect …

Hate speech is one type of harmful online content which directly attacks or promotes hate
towards a group or an individual member based on their actual or perceived aspects of …

We present a human-and-model-in-the-loop process for dynamically generating datasets
and training better performing and more robust hate detection models. We provide a new …

Labelled data is the foundation of most natural language processing tasks. However,
labelling data is difficult and there often are diverse valid beliefs about what the correct data …

Models trained on large unlabeled corpora of human interactions will learn patterns and
mimic behaviors therein, which include offensive or otherwise toxic behavior and unwanted …

The social impact of natural language processing and its applications has received
increasing attention. In this position paper, we focus on the problem of safety for end-to-end …

Social media has effectively replaced traditional forms of communication and marketing. As
these platforms allow for the free expression of ideas and facts through text, images, and …