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 …

Abstract The Hate Speech Detection (HaSpeeDe 2) task is the second edition of a shared
task on the detection of hateful content in Italian Twitter messages. HaSpeeDe 2 is …

We present the results and main findings of SemEval-2020 Task 12 on Multilingual
Offensive Language Identification in Social Media (OffensEval 2020). The task involves …

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 …

The paper describes the organization, goals, and results of the sE**sm Identification in
Social neTworks (EXIST) challenge, a shared task proposed for the first time at IberLEF …

Abstract Language models have revolutionized the field of NLP. However, language models
capture and proliferate hurtful stereotypes, especially in text generation. Our results show …

An increasing awareness of biased patterns in natural language processing resources such
as BERT has motivated many metrics to quantify 'bias' and 'fairness' in these resources …

In the last decade, the need to detect automatically irony to correctly recognize the sentiment
and hate speech involved in online texts increased the investigation on humorous figures of …

The increasing popularity of social media platforms such as Twitter and Facebook has led to
a rise in the presence of hate and aggressive speech on these platforms. Despite the …

Abusive language detection is an unsolved and challenging problem for the NLP
community. Recent literature suggests various approaches to distinguish between different …