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 …

Responsible innovation on large-scale Language Models (LMs) requires foresight into and
in-depth understanding of the risks these models may pose. This paper develops a …

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

Large language models (LM) generate remarkably fluent text and can be efficiently adapted
across NLP tasks. Measuring and guaranteeing the quality of generated text in terms of …

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 …

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 …

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 …

A revealing and gripping investigation into how social media platforms police what we post
online—and the large societal impact of these decisions Most users want their Twitter feed …

The scientific study of hate speech, from a computer science point of view, is recent. This
survey organizes and describes the current state of the field, providing a structured overview …