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.

近年来，反言论已经成为打击网络仇恨的最有希望的策略之一。这些非升级回应在保护用户言论自由的同时，可以在减少在线和离线暴力方面产生明显影响。本文提供了关于进行反言论研究的指南，通过详细的实例描述了相关步骤，并提供了从自然语言处理研究的最佳实践。最后，我们讨论了自然语言处理中反言论研究的挑战和未来方向。

应对仇恨言论的自然语言处理：概述和操作指南

NLP for Counterspeech against Hate: A Survey and How-To Guide

With the high prevalence of offensive language against minorities in social
media, counter-hate speeches (CHS) generation is considered an automatic way of
tackling this challenge. The CHS is supposed to appear as a third voice to
educate people and keep the social [red lines bold] without limiting the
principles of freedom of speech. In this paper, we review the most important
research in the past and present with a main focus on methodologies, collected
datasets and statistical analysis CHS's impact on social media. The CHS
generation is based on the optimistic assumption that any attempt to intervene
the hate speech in social media can play a positive role in this context.
Beyond that, previous works ignored the investigation of the sequence of
comments before and after the CHS. However, the positive impact is not
guaranteed, as shown in some previous works. To the best of our knowledge, no
attempt has been made to survey the related work to compare the past research
in terms of CHS's impact on social media. We take the first step in this
direction by providing a comprehensive review on related works and categorizing
them based on different factors including impact, methodology, data source,
etc.

本文综述了反对仇恨言论生成的最重要研究，重点关注方法学、数据集以及统计分析对社交媒体的影响。