Revisiting Hate Speech Benchmarks: From Data Curation to System Deployment

• URL: http://arxiv.org/abs/2306.01105v2
• Date: Thu, 15 Jun 2023 12:37:34 GMT
• Title: Revisiting Hate Speech Benchmarks: From Data Curation to System Deployment
• Authors: Atharva Kulkarni, Sarah Masud, Vikram Goyal, Tanmoy Chakraborty
• Abstract summary: We present GOTHate, a large-scale code-mixed crowdsourced dataset of around 51k posts for hate speech detection from Twitter. We benchmark it with 10 recent baselines and investigate how adding endogenous signals enhances the hate speech detection task. Our solution HEN-mBERT is a modular, multilingual, mixture-of-experts model that enriches the linguistic subspace with latent endogenous signals.
• Score: 26.504056750529124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Social media is awash with hateful content, much of which is often veiled with linguistic and topical diversity. The benchmark datasets used for hate speech detection do not account for such divagation as they are predominantly compiled using hate lexicons. However, capturing hate signals becomes challenging in neutrally-seeded malicious content. Thus, designing models and datasets that mimic the real-world variability of hate warrants further investigation. To this end, we present GOTHate, a large-scale code-mixed crowdsourced dataset of around 51k posts for hate speech detection from Twitter. GOTHate is neutrally seeded, encompassing different languages and topics. We conduct detailed comparisons of GOTHate with the existing hate speech datasets, highlighting its novelty. We benchmark it with 10 recent baselines. Our extensive empirical and benchmarking experiments suggest that GOTHate is hard to classify in a text-only setup. Thus, we investigate how adding endogenous signals enhances the hate speech detection task. We augment GOTHate with the user's timeline information and ego network, bringing the overall data source closer to the real-world setup for understanding hateful content. Our proposed solution HEN-mBERT is a modular, multilingual, mixture-of-experts model that enriches the linguistic subspace with latent endogenous signals from history, topology, and exemplars. HEN-mBERT transcends the best baseline by 2.5% and 5% in overall macro-F1 and hate class F1, respectively. Inspired by our experiments, in partnership with Wipro AI, we are developing a semi-automated pipeline to detect hateful content as a part of their mission to tackle online harm.

Related papers

• Silent Signals, Loud Impact: LLMs for Word-Sense Disambiguation of Coded Dog Whistles [47.61526125774749]
  A dog whistle is a form of coded communication that carries a secondary meaning to specific audiences and is often weaponized for racial and socioeconomic discrimination. We present an approach for word-sense disambiguation of dog whistles from standard speech using Large Language Models (LLMs) We leverage this technique to create a dataset of 16,550 high-confidence coded examples of dog whistles used in formal and informal communication.
  arXiv  Detail & Related papers  (2024-06-10T23:09:19Z)
• Exploiting Hatred by Targets for Hate Speech Detection on Vietnamese Social Media Texts [0.0]
  We first introduce the ViTHSD - a targeted hate speech detection dataset for Vietnamese Social Media Texts. The dataset contains 10K comments, each comment is labeled to specific targets with three levels: clean, offensive, and hate. The inter-annotator agreement obtained from the dataset is 0.45 by Cohen's Kappa index, which is indicated as a moderate level.
  arXiv  Detail & Related papers  (2024-04-30T04:16:55Z)
• Into the LAIONs Den: Investigating Hate in Multimodal Datasets [67.21783778038645]
  This paper investigates the effect of scaling datasets on hateful content through a comparative audit of two datasets: LAION-400M and LAION-2B. We found that hate content increased by nearly 12% with dataset scale, measured both qualitatively and quantitatively. We also found that filtering dataset contents based on Not Safe For Work (NSFW) values calculated based on images alone does not exclude all the harmful content in alt-text.
  arXiv  Detail & Related papers  (2023-11-06T19:00:05Z)
• 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)
• 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)
• 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)
• Hate speech detection using static BERT embeddings [0.9176056742068814]
  Hate speech is emerging as a major concern, where it expresses abusive speech that targets specific group characteristics. In this paper, we analyze the performance of hate speech detection by replacing or integrating the word embeddings. In comparison to fine-tuned BERT, one metric that significantly improved is specificity.
  arXiv  Detail & Related papers  (2021-06-29T16:17:10Z)
• An Information Retrieval Approach to Building Datasets for Hate Speech Detection [3.587367153279349]
  A common practice is to only annotate tweets containing known hate words'' A second challenge is that definitions of hate speech tend to be highly variable and subjective. Our key insight is that the rarity and subjectivity of hate speech are akin to that of relevance in information retrieval (IR)
  arXiv  Detail & Related papers  (2021-06-17T19:25:39Z)
• Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application [63.10266319378212]
  We propose a method for measuring complex variables on a continuous, interval spectrum by combining supervised deep learning with the Constructing Measures approach to faceted Rasch item response theory (IRT) We demonstrate this new method on a dataset of 50,000 social media comments sourced from YouTube, Twitter, and Reddit and labeled by 11,000 U.S.-based Amazon Mechanical Turk workers.
  arXiv  Detail & Related papers  (2020-09-22T02:15:05Z)
• Towards Hate Speech Detection at Large via Deep Generative Modeling [4.080068044420974]
  Hate speech detection is a critical problem in social media platforms. We present a dataset of 1 million realistic hate and non-hate sequences, produced by a deep generative language model. We demonstrate consistent and significant performance improvements across five public hate speech datasets.
  arXiv  Detail & Related papers  (2020-05-13T15:25:59Z)

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.