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 …

The pervasiveness of abusive content on the internet can lead to severe psychological and
physical harm. Significant effort in Natural Language Processing (NLP) research has been …

Recent directions for offensive language detection are hierarchical modeling, identifying the
type and the target of offensive language, and interpretability with offensive span annotation …

Detecting online hate is a complex task, and low-performing models have harmful
consequences when used for sensitive applications such as content moderation. Emoji …

Although media bias detection is a complex multi-task problem, there is, to date, no unified
benchmark grouping these evaluation tasks. We introduce the Media Bias Identification …

Warning: This paper contains content that may be offensive or upsetting. Understanding the
harms and offensiveness of statements requires reasoning about the social and situational …

Hate speech is plaguing the cyberspace along with user-generated content. This paper
investigates the role of conversational context in the annotation and detection of online hate …

The widespread of offensive content online, such as hate speech and cyber-bullying, is a
global phenomenon. This has sparked interest in the artificial intelligence (AI) and natural …

While social media offers freedom of self-expression, abusive language carry significant
negative social impact. Driven by the importance of the issue, research in the automated …