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 …

An estimated 100,000 people work today as commercial content moderators. These
moderators are often exposed to disturbing content, which can lead to lasting psychological …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

Majority voting and averaging are common approaches used to resolve annotator
disagreements and derive single ground truth labels from multiple annotations. However …

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 …

Building equitable and inclusive NLP technologies demands consideration of whether and
how social attitudes are represented in ML models. In particular, representations encoded in …

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

The freedom of expression given by social media has a dark side: the growing proliferation
of abusive contents on these platforms. Misogynistic speech is a kind of abusive language …

Despite inextricable ties between race and language, little work has considered race in NLP
research and development. In this work, we survey 79 papers from the ACL anthology that …