Towards generalisable hate speech detection: a review on obstacles and solutions

• URL: http://arxiv.org/abs/2102.08886v1
• Date: Wed, 17 Feb 2021 17:27:48 GMT
• Title: Towards generalisable hate speech detection: a review on obstacles and solutions
• Authors: Wenjie Yin, Arkaitz Zubiaga
• Abstract summary: This survey paper attempts to summarise how generalisable existing hate speech detection models are. It sums up existing attempts at addressing the main obstacles, and then proposes directions of future research to improve generalisation in hate speech detection.
• Score: 6.531659195805749
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hate speech is one type of harmful online content which directly attacks or promotes hate towards a group or an individual member based on their actual or perceived aspects of identity, such as ethnicity, religion, and sexual orientation. With online hate speech on the rise, its automatic detection as a natural language processing task is gaining increasing interest. However, it is only recently that it has been shown that existing models generalise poorly to unseen data. This survey paper attempts to summarise how generalisable existing hate speech detection models are, reason why hate speech models struggle to generalise, sums up existing attempts at addressing the main obstacles, and then proposes directions of future research to improve generalisation in hate speech detection.

Related papers

• Hierarchical Sentiment Analysis Framework for Hate Speech Detection: Implementing Binary and Multiclass Classification Strategy [0.0]
  We propose a new multitask model integrated with shared emotional representations to detect hate speech across the English language. We conclude that utilizing sentiment analysis and a Transformer-based trained model considerably improves hate speech detection across multiple datasets.
  arXiv  Detail & Related papers  (2024-11-03T04:11:33Z)
• 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)
• HARE: Explainable Hate Speech Detection with Step-by-Step Reasoning [29.519687405350304]
  We introduce a hate speech detection framework, HARE, which harnesses the reasoning capabilities of large language models (LLMs) to fill gaps in explanations of hate speech. Experiments on SBIC and Implicit Hate benchmarks show that our method, using model-generated data, consistently outperforms baselines. Our method enhances the explanation quality of trained models and improves generalization to unseen datasets.
  arXiv  Detail & Related papers  (2023-11-01T06:09:54Z)
• Hate Speech Detection via Dual Contrastive Learning [25.878271501274245]
  We propose a novel dual contrastive learning framework for hate speech detection. Our framework jointly optimize the self-supervised and the supervised contrastive learning loss for capturing span-level information. We conduct experiments on two publicly available English datasets, and experimental results show that the proposed model outperforms the state-of-the-art models.
  arXiv  Detail & Related papers  (2023-07-10T13:23:36Z)
• When the Majority is Wrong: Modeling Annotator Disagreement for Subjective Tasks [45.14664901245331]
  A crucial problem in hate speech detection is determining whether a statement is offensive to a demographic group. We construct a model that predicts individual annotator ratings on potentially offensive text. We find that annotator ratings can be predicted using their demographic information and opinions on online content.
  arXiv  Detail & Related papers  (2023-05-11T07:55:20Z)
• 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)
• Leveraging World Knowledge in Implicit Hate Speech Detection [5.5536024561229205]
  We show that real world knowledge about entity mentions in a text does help models better detect hate speech. We also discuss cases where real world knowledge does not add value to hate speech detection.
  arXiv  Detail & Related papers  (2022-12-28T21:23:55Z)
• 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)
• Addressing the Challenges of Cross-Lingual Hate Speech Detection [115.1352779982269]
  In this paper we focus on cross-lingual transfer learning to support hate speech detection in low-resource languages. We leverage cross-lingual word embeddings to train our neural network systems on the source language and apply it to the target language. We investigate the issue of label imbalance of hate speech datasets, since the high ratio of non-hate examples compared to hate examples often leads to low model performance.
  arXiv  Detail & Related papers  (2022-01-15T20:48:14Z)
• Characterizing the adversarial vulnerability of speech self-supervised learning [95.03389072594243]
  We make the first attempt to investigate the adversarial vulnerability of such paradigm under the attacks from both zero-knowledge adversaries and limited-knowledge adversaries. The experimental results illustrate that the paradigm proposed by SUPERB is seriously vulnerable to limited-knowledge adversaries.
  arXiv  Detail & Related papers  (2021-11-08T08:44:04Z)
• Latent Hatred: A Benchmark for Understanding Implicit Hate Speech [22.420275418616242]
  This work introduces a theoretically-justified taxonomy of implicit hate speech and a benchmark corpus with fine-grained labels for each message. We present systematic analyses of our dataset using contemporary baselines to detect and explain implicit hate speech.
  arXiv  Detail & Related papers  (2021-09-11T16:52:56Z)

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.