A Review of Challenges in Machine Learning based Automated Hate Speech Detection

• URL: http://arxiv.org/abs/2209.05294v1
• Date: Mon, 12 Sep 2022 14:56:14 GMT
• Title: A Review of Challenges in Machine Learning based Automated Hate Speech Detection
• Authors: Abhishek Velankar, Hrushikesh Patil, Raviraj Joshi
• Abstract summary: We focus on challenges faced by machine learning or deep learning based solutions to hate speech identification. At the top level, we distinguish between data level, model level, and human level challenges. This survey will help researchers to design their solutions more efficiently in the domain of hate speech detection.
• Score: 0.966840768820136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The spread of hate speech on social media space is currently a serious issue. The undemanding access to the enormous amount of information being generated on these platforms has led people to post and react with toxic content that originates violence. Though efforts have been made toward detecting and restraining such content online, it is still challenging to identify it accurately. Deep learning based solutions have been at the forefront of identifying hateful content. However, the factors such as the context-dependent nature of hate speech, the intention of the user, undesired biases, etc. make this process overcritical. In this work, we deeply explore a wide range of challenges in automatic hate speech detection by presenting a hierarchical organization of these problems. We focus on challenges faced by machine learning or deep learning based solutions to hate speech identification. At the top level, we distinguish between data level, model level, and human level challenges. We further provide an exhaustive analysis of each level of the hierarchy with examples. This survey will help researchers to design their solutions more efficiently in the domain of hate speech detection.

Related papers

• V3Det Challenge 2024 on Vast Vocabulary and Open Vocabulary Object Detection: Methods and Results [142.5704093410454]
  The V3Det Challenge 2024 aims to push the boundaries of object detection research. The challenge consists of two tracks: Vast Vocabulary Object Detection and Open Vocabulary Object Detection. We aim to inspire future research directions in vast vocabulary and open-vocabulary object detection.
  arXiv  Detail & Related papers  (2024-06-17T16:58:51Z)
• 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)
• 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)
• Hate Speech Classification Using SVM and Naive BAYES [0.0]
  Many countries have developed laws to avoid online hate speech. But as online content continues to grow, so does the spread of hate speech. It is important to automatically process the online user contents to detect and remove hate speech.
  arXiv  Detail & Related papers  (2022-03-21T17:15:38Z)
• 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)
• Addressing Hate Speech with Data Science: An Overview from Computer Science Perspective [2.2940141855172027]
  From a computer science perspective, addressing on-line hate speech is a challenging task that is attracting the attention of both industry (mainly social media platform owners) and academia. We provide an overview of state-of-the-art data-science approaches - how they define hate speech, which tasks they solve to mitigate the phenomenon, and how they address these tasks. We summarize the challenges and the open problems in the current data-science research and the future directions in this field.
  arXiv  Detail & Related papers  (2021-03-18T19:19:44Z)
• DeepHate: Hate Speech Detection via Multi-Faceted Text Representations [8.192671048046687]
  DeepHate is a novel deep learning model that combines multi-faceted text representations such as word embeddings, sentiments, and topical information. We conduct extensive experiments and evaluate DeepHate on three large publicly available real-world datasets.
  arXiv  Detail & Related papers  (2021-03-14T16:11:30Z)
• Speaker De-identification System using Autoencoders and Adversarial Training [58.720142291102135]
  We propose a speaker de-identification system based on adversarial training and autoencoders. Experimental results show that combining adversarial learning and autoencoders increase the equal error rate of a speaker verification system.
  arXiv  Detail & Related papers  (2020-11-09T19:22:05Z)
• Investigating Deep Learning Approaches for Hate Speech Detection in Social Media [20.974715256618754]
  The misuse of freedom of expression has led to the increase of various cyber crimes and anti-social activities. Hate speech is one such issue that needs to be addressed very seriously as otherwise, this could pose threats to the integrity of the social fabrics. In this paper, we proposed deep learning approaches utilizing various embeddings for detecting various types of hate speeches in social media.
  arXiv  Detail & Related papers  (2020-05-29T17:28:46Z)

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.