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 …

Pretrained neural language models (LMs) are prone to generating racist, sexist, or otherwise
toxic language which hinders their safe deployment. We investigate the extent to which …

This work proposes a new challenge set for multimodal classification, focusing on detecting
hate speech in multimodal memes. It is constructed such that unimodal models struggle and …

With the multiplication of social media platforms, which offer anonymity, easy access and
online community formation and online debate, the issue of hate speech detection and …

We investigate how annotators' insensitivity to differences in dialect can lead to racial bias in
automatic hate speech detection models, potentially amplifying harm against minority …

Detecting online hate is a difficult task that even state-of-the-art models struggle with.
Typically, hate speech detection models are evaluated by measuring their performance on …

Technologies for abusive language detection are being developed and applied with little
consideration of their potential biases. We examine racial bias in five different sets of Twitter …

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 …