Leveraging World Knowledge in Implicit Hate Speech Detection

• URL: http://arxiv.org/abs/2212.14100v1
• Date: Wed, 28 Dec 2022 21:23:55 GMT
• Title: Leveraging World Knowledge in Implicit Hate Speech Detection
• Authors: Jessica Lin
• Abstract summary: 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.
• Score: 5.5536024561229205
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While much attention has been paid to identifying explicit hate speech, implicit hateful expressions that are disguised in coded or indirect language are pervasive and remain a major challenge for existing hate speech detection systems. This paper presents the first attempt to apply Entity Linking (EL) techniques to both explicit and implicit hate speech detection, where we show that such real world knowledge about entity mentions in a text does help models better detect hate speech, and the benefit of adding it into the model is more pronounced when explicit entity triggers (e.g., rally, KKK) are present. We also discuss cases where real world knowledge does not add value to hate speech detection, which provides more insights into understanding and modeling the subtleties of hate speech.

Related papers

• 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)
• 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)
• Beyond Plain Toxic: Detection of Inappropriate Statements on Flammable Topics for the Russian Language [76.58220021791955]
  We present two text collections labelled according to binary notion of inapropriateness and a multinomial notion of sensitive topic. To objectivise the notion of inappropriateness, we define it in a data-driven way though crowdsourcing.
  arXiv  Detail & Related papers  (2022-03-04T15:59:06Z)
• 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)
• Textless Speech Emotion Conversion using Decomposed and Discrete Representations [49.55101900501656]
  We decompose speech into discrete and disentangled learned representations, consisting of content units, F0, speaker, and emotion. First, we modify the speech content by translating the content units to a target emotion, and then predict the prosodic features based on these units. Finally, the speech waveform is generated by feeding the predicted representations into a neural vocoder.
  arXiv  Detail & Related papers  (2021-11-14T18:16:42Z)
• Detection of Hate Speech using BERT and Hate Speech Word Embedding with Deep Model [0.5801044612920815]
  This paper investigates the feasibility of leveraging domain-specific word embedding in Bidirectional LSTM based deep model to automatically detect/classify hate speech. The experiments showed that domainspecific word embedding with the Bidirectional LSTM based deep model achieved a 93% f1-score while BERT achieved up to 96% f1-score on a combined balanced dataset from available hate speech datasets.
  arXiv  Detail & Related papers  (2021-11-02T11:42:54Z)
• 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)
• Towards generalisable hate speech detection: a review on obstacles and solutions [6.531659195805749]
  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.
  arXiv  Detail & Related papers  (2021-02-17T17:27:48Z)

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.