The paper "NLP for Counterspeech against Hate: A Survey and How-To Guide" by Helena Bonaldi, Yi-Ling Chung, Gavin Abercrombie, and Marco Guerini provides a comprehensive overview of the use of Natural Language Processing (NLP) in addressing online hate. The authors discuss the importance of counterspeech, which is non-escalatory responses to hate speech that preserve free speech while reducing online and offline violence. They review existing NLP studies and resources on counterspeech, propose best practices, and highlight open challenges in the field. The paper is structured into three main sections: task design, data selection, and evaluation. In the task design section, the authors discuss classification, selection, and generation tasks, emphasizing the importance of aligning these tasks with specific research goals. They also provide practical recommendations for each task, such as using fine-tuning and prompting techniques for generation. The data selection section covers various methods for collecting counterspeech data, including crawling, crowdsourcing, nichesourcing, hybrid approaches, and fully automated methods. The authors recommend considering factors like data size, diversity, and the structure and style of counterspeech when choosing a dataset. The evaluation section addresses the limitations of existing metrics and suggests the need for more comprehensive evaluation methods. It also discusses potential biases in data collection and ethical considerations, such as the mental well-being of researchers and the privacy of counterspeakers. Overall, the paper aims to guide both newcomers and experts in NLP research on counterspeech, providing a step-by-step guide to conducting effective and responsible research in this area.The paper "NLP for Counterspeech against Hate: A Survey and How-To Guide" by Helena Bonaldi, Yi-Ling Chung, Gavin Abercrombie, and Marco Guerini provides a comprehensive overview of the use of Natural Language Processing (NLP) in addressing online hate. The authors discuss the importance of counterspeech, which is non-escalatory responses to hate speech that preserve free speech while reducing online and offline violence. They review existing NLP studies and resources on counterspeech, propose best practices, and highlight open challenges in the field. The paper is structured into three main sections: task design, data selection, and evaluation. In the task design section, the authors discuss classification, selection, and generation tasks, emphasizing the importance of aligning these tasks with specific research goals. They also provide practical recommendations for each task, such as using fine-tuning and prompting techniques for generation. The data selection section covers various methods for collecting counterspeech data, including crawling, crowdsourcing, nichesourcing, hybrid approaches, and fully automated methods. The authors recommend considering factors like data size, diversity, and the structure and style of counterspeech when choosing a dataset. The evaluation section addresses the limitations of existing metrics and suggests the need for more comprehensive evaluation methods. It also discusses potential biases in data collection and ethical considerations, such as the mental well-being of researchers and the privacy of counterspeakers. Overall, the paper aims to guide both newcomers and experts in NLP research on counterspeech, providing a step-by-step guide to conducting effective and responsible research in this area.