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 …

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 …

Data-driven and machine learning based approaches for detecting, categorising and
measuring abusive content such as hate speech and harassment have gained traction due …

Hate speech is considered to be one of the major issues currently plaguing the online social
media. With online hate speech culminating in gruesome scenarios like the Rohingya …

When toxic online communities on mainstream platforms face moderation measures, such
as bans, they may migrate to other platforms with laxer policies or set up their own dedicated …

The outbreak of the COVID-19 pandemic has changed our lives in unprecedented ways. In
the face of the projected catastrophic consequences, many countries have enacted social …