Social networking platforms gained widespread popularity and are used for various activities
like: promoting products, sharing news, achievements and many more. On the other hand, it …

Offensive communications have made their way into social media posts. Using
computational algorithms to distinguish objectionable content is one of the most effective …

The widespread presence of offensive language on social media motivated the
development of systems capable of recognizing such content automatically. Apart from a few …

People are increasingly sharing and expressing their emotions using online social media
platforms such as Twitter, Facebook, and YouTube. An abusive, hateful, threatening, and …

Numerous machine learning (ML) and deep learning (DL)-based approaches have been
proposed to utilize textual data from social media for anti-social behavior analysis like …

Offensive content or hate speech is defined as any form of communication that aims to
annoy, harass, disturb, or anger an individual or community based on factors such as faith …

The pervasiveness of offensive content in social media has become an important reason for
concern for online platforms. With the aim of improving online safety, a large number of …

Online Social Network has let people connect and interact with each other. It does, however,
also provide a platform for online abusers to propagate abusive content. The majority of …

Hate speech detection is an important research area owing to the severe effects of hate
speech on the society. Hence automated hate speech detection based on textual data …

This paper summarizes the shared task on multimodal abusive language detection and
sentiment analysis in Dravidian languages as part of the third Workshop on Speech and …