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

• URL: http://arxiv.org/abs/2403.20103v1
• Date: Fri, 29 Mar 2024 10:32:44 GMT
• Title: NLP for Counterspeech against Hate: A Survey and How-To Guide
• Authors: Helena Bonaldi, Yi-Ling Chung, Gavin Abercrombie, Marco Guerini,
• Abstract summary: We provide a guide for doing research on counterspeech, by describing - with detailed examples - the steps to undertake. We discuss open challenges and future directions of counterspeech research in NLP.
• Score: 11.696212004806263
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Demarked: A Strategy for Enhanced Abusive Speech Moderation through Counterspeech, Detoxification, and Message Management [71.99446449877038]
  We propose a more comprehensive approach called Demarcation scoring abusive speech based on four aspect -- (i) severity scale; (ii) presence of a target; (iii) context scale; (iv) legal scale. Our work aims to inform future strategies for effectively addressing abusive speech online.
  arXiv  Detail & Related papers  (2024-06-27T21:45:33Z)
• What Can Natural Language Processing Do for Peer Review? [173.8912784451817]
  In modern science, peer review is widely used, yet it is hard, time-consuming, and prone to error. Since the artifacts involved in peer review are largely text-based, Natural Language Processing has great potential to improve reviewing. We detail each step of the process from manuscript submission to camera-ready revision, and discuss the associated challenges and opportunities for NLP assistance.
  arXiv  Detail & Related papers  (2024-05-10T16:06:43Z)
• Behind the Counter: Exploring the Motivations and Barriers of Online Counterspeech Writing [6.790819952175892]
  Having been a target of online hate is a key driver of frequent online counterspeech engagement. People differ in their motivations and barriers towards engaging in online counterspeech across different demographic groups.
  arXiv  Detail & Related papers  (2024-03-25T18:56:35Z)
• Combatting Human Trafficking in the Cyberspace: A Natural Language Processing-Based Methodology to Analyze the Language in Online Advertisements [55.2480439325792]
  This project tackles the pressing issue of human trafficking in online C2C marketplaces through advanced Natural Language Processing (NLP) techniques. We introduce a novel methodology for generating pseudo-labeled datasets with minimal supervision, serving as a rich resource for training state-of-the-art NLP models. A key contribution is the implementation of an interpretability framework using Integrated Gradients, providing explainable insights crucial for law enforcement.
  arXiv  Detail & Related papers  (2023-11-22T02:45:01Z)
• Understanding Counterspeech for Online Harm Mitigation [12.104301755723542]
  Counterspeech offers direct rebuttals to hateful speech by challenging perpetrators of hate and showing support to targets of abuse. It provides a promising alternative to more contentious measures, such as content moderation and deplatforming. This paper systematically reviews counterspeech research in the social sciences and compares methodologies and findings with computer science efforts in automatic counterspeech generation.
  arXiv  Detail & Related papers  (2023-07-01T20:54:01Z)
• Surveying (Dis)Parities and Concerns of Compute Hungry NLP Research [75.84463664853125]
  We provide a first attempt to quantify concerns regarding three topics, namely, environmental impact, equity, and impact on peer reviewing. We capture existing (dis)parities between different and within groups with respect to seniority, academia, and industry. We devise recommendations to mitigate found disparities, some of which already successfully implemented.
  arXiv  Detail & Related papers  (2023-06-29T12:44:53Z)
• Pushing the Limits of ChatGPT on NLP Tasks [79.17291002710517]
  Despite the success of ChatGPT, its performances on most NLP tasks are still well below the supervised baselines. In this work, we looked into the causes, and discovered that its subpar performance was caused by the following factors. We propose a collection of general modules to address these issues, in an attempt to push the limits of ChatGPT on NLP tasks.
  arXiv  Detail & Related papers  (2023-06-16T09:40:05Z)
• Learning to Selectively Learn for Weakly-supervised Paraphrase Generation [81.65399115750054]
  We propose a novel approach to generate high-quality paraphrases with weak supervision data. Specifically, we tackle the weakly-supervised paraphrase generation problem by:. obtaining abundant weakly-labeled parallel sentences via retrieval-based pseudo paraphrase expansion. We demonstrate that our approach achieves significant improvements over existing unsupervised approaches, and is even comparable in performance with supervised state-of-the-arts.
  arXiv  Detail & Related papers  (2021-09-25T23:31:13Z)
• Countering Online Hate Speech: An NLP Perspective [34.19875714256597]
  Online toxicity - an umbrella term for online hateful behavior - manifests itself in forms such as online hate speech. The rising mass communication through social media further exacerbates the harmful consequences of online hate speech. This paper presents a holistic conceptual framework on hate-speech NLP countering methods along with a thorough survey on the current progress of NLP for countering online hate speech.
  arXiv  Detail & Related papers  (2021-09-07T08:48:13Z)
• Generate, Prune, Select: A Pipeline for Counterspeech Generation against Online Hate Speech [9.49544185939481]
  Off-the-shelf Natural Language Generation (NLG) methods are limited in that they generate commonplace, repetitive and safe responses. In this paper, we design a three- module pipeline approach to effectively improve the diversity and relevance. Our proposed pipeline first generates various counterspeech candidates by a generative model to promote diversity, then filters the ungrammatical ones using a BERT model, and finally selects the most relevant counterspeech response.
  arXiv  Detail & Related papers  (2021-06-03T06:54:03Z)
• A Weakly Supervised Approach for Classifying Stance in Twitter Replies [11.139350549173953]
  adversarial reactions are prevalent in online conversations. Inferring those adverse views (or stance) from the text in replies is difficult. We propose a weakly-supervised approach to predict the stance in Twitter replies.
  arXiv  Detail & Related papers  (2021-03-12T06:02:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.