Detecting online toxicity has always been a challenge due to its inherent subjectivity. Factors
such as the context, geography, socio-political climate, and background of the producers …

With the proliferation of social media platforms that provide anonymity, easy access, online
community development, and online debate, detecting and tracking hate speech has …

The machine learning (ML) research community has landed on automated hate speech
detection as the vital tool in the mitigation of bad behavior online. However, it is not clear that …

Counterspeech has been demonstrated to be an efficacious approach for combating hate
speech. While various conventional and controlled approaches have been studied in recent …

The spread of hate speech on social media space is currently a serious issue. The
undemanding access to the enormous amount of information being generated on these …

Social media is awash with hateful content, much of which is often veiled with linguistic and
topical diversity. The benchmark datasets used for hate speech detection do not account for …

This paper proposes a multi-channel convolutional neural network (MC-CNN) for classifying
memes and non-memes. Our architecture is trained and validated on a challenging dataset …

This article provides an overview of a shared task to identify contextual hate speech in social
media conversations. This task intends to analyze how context within a conversation in …

Hate Speech Detection in Arabic presents a multifaceted challenge due to the broad and
diverse linguistic terrain. With its multiple dialects and rich cultural subtleties, Arabic requires …

Social media has emerged as a widespread phenomenon, with numerous users engaging
in observing, creating, and distributing content. The growing content has led to user conflicts …