NLP for Counterspeech against Hate: A Survey and How-To Guide (2024)

Abstract: 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.

## introduction online spaces provide fertile ground for the diffusion of hateful content, which is often interlinked with episodes of offline violence @xcite . both witnessing and receiving hateful content can be detrimental to the mental health of victims and create a sense of insecurity @xcite @xcite , determining the need to mitigate hate. in this context, counterspeech represents a promising strategy to oppose online hate, since it can be more effective than other moderation procedures @xcite , while also protecting free speech @xcite . because of its potential effectiveness, counterspeech has been investigated by non-governmental organisations (ngos) as a possible strategy to fight online hate. an example of hate speech (hs) and counterspeech (cs) from @xcite is shown here: foot_0 hs: women are basically childlike, they remain this way most of their lives. soft and emotional. it has devastated our once great patriarchal civilizations. cs: without softness and emotions there would be just brutality and cruelty. not all women are soft and emotional and many men have these characteristics. to perpetuate these socially constructed gender profiles maintains patriarchal norms which oppress both men and women. given the amount of hateful content produced, an increasing number of natural language processing (nlp) studies have begun to address the task of automatic counterspeech classification and generation. however, the settled definitions and best practices required to unify these efforts are still missing. while prior surveys largely focused on the effectiveness of deploying counterspeech in the real world @xcite @xcite , we offer a complete step-by-step guide on how to conduct nlp research on counterspeech for both newcomers and experts. in particular, we extensively review existing nlp studies and resources on counterspeech, propose common concepts and best practices, and point out the limitations and open challenges of what has been done so far. after providing some background ( §2), the guide is articulated in three steps: task design, data selection and evaluation ( §3, §4, §5, respectively). 2 finally, we discuss the open challenges in the field. a complete description of the review methodology we used is provided in appendix a.1. ## background to better frame the concept of counterspeech we review definitions that have been proposed for it, the strategies that can be adopted, and several related tasks. ## step 1: design your task the first step is to select which counterspeech task(s) to tackle. we discuss studies covering classification, selection and generation, and derive possible best practices from them. ## step 2: select the data after task design, the next choice is whether to collect a new dataset or to use an already existing one. we will discuss the use-cases of the main counterspeech collection procedures, and then detail the characteristics of available counterspeech datasets. ## step 3: evaluate next, we look at the literature on evaluating counterspeech based on the tasks discussed in §3. 10 ## open challenges drawing from the surveyed literature, we highlight key open challenges in counterspeech research. language and culture. hate speech is not only linguistically, but also culturally specific. therefore, it requires culturally specific responses. for example, in spanish, the same words can convey discriminatory connotations depending on the country in which they are used @xcite . moreover, the same groups can be subject to different stereotypes associated with the historical events of their location @xcite . ## conclusion we presented a thorough review of 43 nlp studies on counterspeech. this is organised as a step-bystep guide, intended for those approaching counterspeech from an nlp perspective. first, we framed counterspeech and its strategies, distinguishing it from other similar tasks. then, we structured the subsequent sections as progressive steps to undertake when approaching counterspeech research in nlp: in these sections, we relied on the literature to provide insights into the consequences each choice might imply. finally, we point out open challenges in the field. counterspeech represents a promising approach to tackling online hate, and nlp can potentially provide the tools to make it scalable. however, an efficient system is not necessarily a good system: researchers operating in this area must be aware of the consequences entailed by each of their choices, to avoid spreading further harm.