What is the social benefit of hate speech detection research? A Systematic Review

• URL: http://arxiv.org/abs/2409.17467v1
• Date: Thu, 26 Sep 2024 01:57:27 GMT
• Title: What is the social benefit of hate speech detection research? A Systematic Review
• Authors: Sidney Gig-Jan Wong,
• Abstract summary: We argue the absence of ethical frameworks have contributed to this rift between current practice and best practice. By adopting appropriate ethical frameworks, NLP researchers may enable the social impact potential of hate speech research.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While NLP research into hate speech detection has grown exponentially in the last three decades, there has been minimal uptake or engagement from policy makers and non-profit organisations. We argue the absence of ethical frameworks have contributed to this rift between current practice and best practice. By adopting appropriate ethical frameworks, NLP researchers may enable the social impact potential of hate speech research. This position paper is informed by reviewing forty-eight hate speech detection systems associated with thirty-seven publications from different venues.

Related papers

• Can NLP Tackle Hate Speech in the Real World? Stakeholder-Informed Feedback and Survey on Counterspeech [9.25125378244369]
  This paper presents a systematic review of 74 NLP studies on counterspeech.<n>We analyse the extent to which stakeholder participation influences dataset creation, model development, and evaluation.<n>Our findings reveal a growing disconnect between current NLP research and the needs of communities most impacted by toxic online content.
  arXiv  Detail & Related papers  (2025-08-06T17:04:58Z)
• HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation [67.69631485036665]
  We conduct a comprehensive examination of hate speech regulations and strategies from three perspectives.<n>Our findings reveal significant inconsistencies in hate speech definitions and moderation practices across jurisdictions.<n>We suggest ideas and research direction for further exploration of a unified framework for automated hate speech moderation.
  arXiv  Detail & Related papers  (2025-07-06T11:25:23Z)
• Stereotype Detection in Natural Language Processing [47.91542090964054]
  Stereotypes influence social perceptions and can escalate into discrimination and violence.<n>This work is presented a survey of existing research, analyzing definitions from psychology, sociology, and philosophy.<n>Findings emphasize stereotype detection as a potential early-monitoring tool to prevent bias escalation and the rise of hate speech.
  arXiv  Detail & Related papers  (2025-05-23T09:03:56Z)
• Towards Unsupervised Speech Recognition Without Pronunciation Models [57.222729245842054]
  Most languages lack sufficient paired speech and text data to effectively train automatic speech recognition systems. We propose the removal of reliance on a phoneme lexicon to develop unsupervised ASR systems. We experimentally demonstrate that an unsupervised speech recognizer can emerge from joint speech-to-speech and text-to-text masked token-infilling.
  arXiv  Detail & Related papers  (2024-06-12T16:30:58Z)
• Hostile Counterspeech Drives Users From Hate Subreddits [1.5035331281822]
  We analyze the effect of counterspeech on newcomers within hate subreddits on Reddit. Non-hostile counterspeech is ineffective at keeping users from fully disengaging from these hate subreddits. A single hostile counterspeech comment substantially reduces both future likelihood of engagement.
  arXiv  Detail & Related papers  (2024-05-28T17:12:41Z)
• An Investigation of Large Language Models for Real-World Hate Speech Detection [46.15140831710683]
  A major limitation of existing methods is that hate speech detection is a highly contextual problem. Recently, large language models (LLMs) have demonstrated state-of-the-art performance in several natural language tasks. Our study reveals that a meticulously crafted reasoning prompt can effectively capture the context of hate speech.
  arXiv  Detail & Related papers  (2024-01-07T00:39:33Z)
• HateRephrase: Zero- and Few-Shot Reduction of Hate Intensity in Online Posts using Large Language Models [4.9711707739781215]
  This paper investigates an approach of suggesting a rephrasing of potential hate speech content even before the post is made. We develop 4 different prompts based on task description, hate definition, few-shot demonstrations and chain-of-thoughts. We find that GPT-3.5 outperforms the baseline and open-source models for all the different kinds of prompts.
  arXiv  Detail & Related papers  (2023-10-21T12:18:29Z)
• Thesis Distillation: Investigating The Impact of Bias in NLP Models on Hate Speech Detection [6.2548734896918505]
  This paper is a summary of the work done in my PhD thesis. I investigate the impact of bias in NLP models on the task of hate speech detection from three perspectives.
  arXiv  Detail & Related papers  (2023-08-31T08:40:41Z)
• A Group-Specific Approach to NLP for Hate Speech Detection [2.538209532048867]
  We propose a group-specific approach to NLP for online hate speech detection. We analyze historical data about discrimination against a protected group to better predict spikes in hate speech against that group. We demonstrate this approach through a case study on NLP for detection of antisemitic hate speech.
  arXiv  Detail & Related papers  (2023-04-21T19:08:49Z)
• CoSyn: Detecting Implicit Hate Speech in Online Conversations Using a Context Synergized Hyperbolic Network [52.85130555886915]
  CoSyn is a context-synergized neural network that explicitly incorporates user- and conversational context for detecting implicit hate speech in online conversations. We show that CoSyn outperforms all our baselines in detecting implicit hate speech with absolute improvements in the range of 1.24% - 57.8%.
  arXiv  Detail & Related papers  (2023-03-02T17:30:43Z)
• Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest [70.02478301291264]
  Social media platforms curate access to information and opportunities, and so play a critical role in shaping public discourse. Prior studies have used black-box methods to show that these algorithms can lead to biased or discriminatory outcomes. We propose a new method for platform-supported auditing that can meet the goals of the proposed legislation.
  arXiv  Detail & Related papers  (2022-07-18T17:32:35Z)
• Deep Learning for Hate Speech Detection: A Comparative Study [54.42226495344908]
  We present here a large-scale empirical comparison of deep and shallow hate-speech detection methods. Our goal is to illuminate progress in the area, and identify strengths and weaknesses in the current state-of-the-art. In doing so we aim to provide guidance as to the use of hate-speech detection in practice, quantify the state-of-the-art, and identify future research directions.
  arXiv  Detail & Related papers  (2022-02-19T03:48:20Z)
• 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)
• Case Study: Deontological Ethics in NLP [119.53038547411062]
  We study one ethical theory, namely deontological ethics, from the perspective of NLP. In particular, we focus on the generalization principle and the respect for autonomy through informed consent. We provide four case studies to demonstrate how these principles can be used with NLP systems.
  arXiv  Detail & Related papers  (2020-10-09T16:04:51Z)

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.