NLP for Counterspeech against Hate: A Survey and How-To Guide
Helena Bonaldi Yi-Ling Chung Gavin Abercrombie Marco Guerini
In recent years, counterspeech has emerged as one of the most promising
strategies to fight online hate. These non-escalatory responses tackle online
abuse while preserving the freedom of speech of the users, and can have a
tangible impact in reducing online and offline violence. Recently, there has
been growing interest from the Natural Language Processing (NLP) community in
addressing the challenges of analysing, collecting, classifying, and
automatically generating counterspeech, to reduce the huge burden of manually
producing it. In particular, researchers have taken different directions in
addressing these challenges, thus providing a variety of related tasks and
resources. In this paper, we provide a guide for doing research on
counterspeech, by describing - with detailed examples - the steps to undertake,
and providing best practices that can be learnt from the NLP studies on this
topic. Finally, we discuss open challenges and future directions of
counterspeech research in NLP.