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 …

The proliferation of social media enables people to express their opinions widely online.
However, at the same time, this has resulted in the emergence of conflict and hate, making …

Despite inextricable ties between race and language, little work has considered race in NLP
research and development. In this work, we survey 79 papers from the ACL anthology that …

Although there is an unprecedented effort to provide adequate responses in terms of laws
and policies to hate content on social media platforms, dealing with hatred online is still a …

The spread of COVID-19 has sparked racism, hate, and xenophobia in social media
targeted at Chinese and broader Asian communities. However, little is known about how …

With the rapid development in mobile computing and Web technologies, online hate speech
has been increasingly spread in social network platforms since it's easy to post any …

Hate content in social media is ever increasing. While Facebook, Twitter, Google have
attempted to take several steps to tackle the hateful content, they have mostly been …

Dialogue models trained on human conversations inadvertently learn to generate toxic
responses. In addition to producing explicitly offensive utterances, these models can also …

Online social media platforms generally attempt to mitigate hateful expressions, as these
comments can be detrimental to the health of the community. However, automatically …

Online abusive behavior affects millions and the NLP community has attempted to mitigate
this problem by developing technologies to detect abuse. However, current methods have …