RV-HATE: Reinforced Multi-Module Voting for Implicit Hate Speech Detection

• URL: http://arxiv.org/abs/2510.10971v1
• Date: Mon, 13 Oct 2025 03:21:51 GMT
• Title: RV-HATE: Reinforced Multi-Module Voting for Implicit Hate Speech Detection
• Authors: Yejin Lee, Hyeseon Ahn, Yo-Sub Han,
• Abstract summary: RV-HATE is a detection framework designed to account for the dataset-specific characteristics of each hate speech dataset.<n>The framework employs reinforcement learning to optimize weights that determine the contribution of each module for a given dataset.<n>Our approach effectively addresses implicit hate speech and achieves superior performance compared to conventional static methods.
• Score: 7.1932638143867775
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Hate speech remains prevalent in human society and continues to evolve in its forms and expressions. Modern advancements in internet and online anonymity accelerate its rapid spread and complicate its detection. However, hate speech datasets exhibit diverse characteristics primarily because they are constructed from different sources and platforms, each reflecting different linguistic styles and social contexts. Despite this diversity, prior studies on hate speech detection often rely on fixed methodologies without adapting to data-specific features. We introduce RV-HATE, a detection framework designed to account for the dataset-specific characteristics of each hate speech dataset. RV-HATE consists of multiple specialized modules, where each module focuses on distinct linguistic or contextual features of hate speech. The framework employs reinforcement learning to optimize weights that determine the contribution of each module for a given dataset. A voting mechanism then aggregates the module outputs to produce the final decision. RV-HATE offers two primary advantages: (1)~it improves detection accuracy by tailoring the detection process to dataset-specific attributes, and (2)~it also provides interpretable insights into the distinctive features of each dataset. Consequently, our approach effectively addresses implicit hate speech and achieves superior performance compared to conventional static methods. Our code is available at https://github.com/leeyejin1231/RV-HATE.

Related papers

• Unifying Heterogeneous Multi-Modal Remote Sensing Detection Via Language-Pivoted Pretraining [59.2578488860426]
  Heterogeneous multi-modal remote sensing object detection aims to accurately detect objects from diverse sensors.<n>Existing approaches largely adopt a late alignment paradigm, in which modality alignment and task-specific optimization are entangled during downstream fine-tuning.<n>We propose BabelRS, a unified language-pivoted pretraining framework that explicitly decouples modality alignment from downstream task learning.
  arXiv  Detail & Related papers  (2026-03-02T11:38:12Z)
• AHELM: A Holistic Evaluation of Audio-Language Models [78.20477815156484]
  multimodal audio-language models (ALMs) take interleaved audio and text as input and output text.<n>AHELM is a benchmark that aggregates various datasets -- including 2 new synthetic audio-text datasets called PARADE and CoRe-Bench.<n>We also standardize the prompts, inference parameters, and evaluation metrics to ensure equitable comparisons across models.
  arXiv  Detail & Related papers  (2025-08-29T07:40:39Z)
• A Unified Multi-Task Learning Architecture for Hate Detection Leveraging User-Based Information [23.017068553977982]
  Hate speech, offensive language, aggression, racism, sexism, and other abusive language are common phenomena in social media. There is a need for Artificial Intelligence(AI)based intervention which can filter hate content at scale. This paper introduces a unique model that improves hate speech identification for the English language by utilising intra-user and inter-user-based information.
  arXiv  Detail & Related papers  (2024-11-11T10:37:11Z)
• 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)
• Hate Speech Detection Using Cross-Platform Social Media Data In English and German Language [6.200058263544999]
  This study focuses on detecting bilingual hate speech in YouTube comments. We include factors such as content similarity, definition similarity, and common hate words to measure the impact of datasets on performance. The best performance was obtained by combining datasets from YouTube comments, Twitter, and Gab with an F1-score of 0.74 and 0.68 for English and German YouTube comments.
  arXiv  Detail & Related papers  (2024-10-02T10:22:53Z)
• LLMs and Finetuning: Benchmarking cross-domain performance for hate speech detection [9.166963162285064]
  This study investigates the effectiveness and adaptability of pre-trained and fine-tuned Large Language Models (LLMs) in identifying hate speech.<n>LLMs offer a huge advantage over the state-of-the-art even without pretraining.
  arXiv  Detail & Related papers  (2023-10-29T10:07:32Z)
• On the Challenges of Building Datasets for Hate Speech Detection [0.0]
  We first analyze the issues surrounding hate speech detection through a data-centric lens. We then outline a holistic framework to encapsulate the data creation pipeline across seven broad dimensions.
  arXiv  Detail & Related papers  (2023-09-06T11:15:47Z)
• 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)
• Selecting and combining complementary feature representations and classifiers for hate speech detection [6.745479230590518]
  Hate speech is a major issue in social networks due to the high volume of data generated daily. Recent works demonstrate the usefulness of machine learning (ML) in dealing with the nuances required to distinguish between hateful posts from just sarcasm or offensive language. This work argues that a combination of multiple feature extraction techniques and different classification models is needed.
  arXiv  Detail & Related papers  (2022-01-18T03:46:49Z)
• 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)
• SPLAT: Speech-Language Joint Pre-Training for Spoken Language Understanding [61.02342238771685]
  Spoken language understanding requires a model to analyze input acoustic signal to understand its linguistic content and make predictions. Various pre-training methods have been proposed to learn rich representations from large-scale unannotated speech and text. We propose a novel semi-supervised learning framework, SPLAT, to jointly pre-train the speech and language modules.
  arXiv  Detail & Related papers  (2020-10-05T19:29:49Z)
• 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)

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.