An Online Multilingual Hate speech Recognition System

• URL: http://arxiv.org/abs/2011.11523v3
• Date: Tue, 22 Dec 2020 18:08:11 GMT
• Title: An Online Multilingual Hate speech Recognition System
• Authors: Neeraj Vashistha, Arkaitz Zubiaga, Shanky Sharma
• Abstract summary: We analyse six datasets by combining them into a single homogeneous dataset and classify them into three classes, abusive, hateful or neither. We create a tool which identifies and scores a page with effective metric in near-real time and uses the same as feedback to re-train our model. We prove the competitive performance of our multilingual model on two langauges, English and Hindi, leading to comparable or superior performance to most monolingual models.
• Score: 13.87667165678441
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The exponential increase in the use of the Internet and social media over the last two decades has changed human interaction. This has led to many positive outcomes, but at the same time it has brought risks and harms. While the volume of harmful content online, such as hate speech, is not manageable by humans, interest in the academic community to investigate automated means for hate speech detection has increased. In this study, we analyse six publicly available datasets by combining them into a single homogeneous dataset and classify them into three classes, abusive, hateful or neither. We create a baseline model and we improve model performance scores using various optimisation techniques. After attaining a competitive performance score, we create a tool which identifies and scores a page with effective metric in near-real time and uses the same as feedback to re-train our model. We prove the competitive performance of our multilingual model on two langauges, English and Hindi, leading to comparable or superior performance to most monolingual models.

Related papers

• 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)
• SpeechAlign: Aligning Speech Generation to Human Preferences [51.684183257809075]
  We introduce SpeechAlign, an iterative self-improvement strategy that aligns speech language models to human preferences. We show that SpeechAlign can bridge the distribution gap and facilitate continuous self-improvement of the speech language model.
  arXiv  Detail & Related papers  (2024-04-08T15:21:17Z)
• Hate Speech and Offensive Language Detection using an Emotion-aware Shared Encoder [1.8734449181723825]
  Existing works on hate speech and offensive language detection produce promising results based on pre-trained transformer models. This paper addresses a multi-task joint learning approach which combines external emotional features extracted from another corpora. Our findings demonstrate that emotional knowledge helps to more reliably identify hate speech and offensive language across datasets.
  arXiv  Detail & Related papers  (2023-02-17T09:31:06Z)
• Chain of Hindsight Aligns Language Models with Feedback [62.68665658130472]
  We propose a novel technique, Chain of Hindsight, that is easy to optimize and can learn from any form of feedback, regardless of its polarity. We convert all types of feedback into sequences of sentences, which are then used to fine-tune the model. By doing so, the model is trained to generate outputs based on feedback, while learning to identify and correct negative attributes or errors.
  arXiv  Detail & Related papers  (2023-02-06T10:28:16Z)
• Countering Malicious Content Moderation Evasion in Online Social Networks: Simulation and Detection of Word Camouflage [64.78260098263489]
  Twisting and camouflaging keywords are among the most used techniques to evade platform content moderation systems. This article contributes significantly to countering malicious information by developing multilingual tools to simulate and detect new methods of evasion of content.
  arXiv  Detail & Related papers  (2022-12-27T16:08:49Z)
• A New Generation of Perspective API: Efficient Multilingual Character-level Transformers [66.9176610388952]
  We present the fundamentals behind the next version of the Perspective API from Google Jigsaw. At the heart of the approach is a single multilingual token-free Charformer model. We demonstrate that by forgoing static vocabularies, we gain flexibility across a variety of settings.
  arXiv  Detail & Related papers  (2022-02-22T20:55:31Z)
• 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)
• Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models [86.02610674750345]
  Adversarial GLUE (AdvGLUE) is a new multi-task benchmark to explore and evaluate the vulnerabilities of modern large-scale language models under various types of adversarial attacks. We apply 14 adversarial attack methods to GLUE tasks to construct AdvGLUE, which is further validated by humans for reliable annotations. All the language models and robust training methods we tested perform poorly on AdvGLUE, with scores lagging far behind the benign accuracy.
  arXiv  Detail & Related papers  (2021-11-04T12:59:55Z)
• Cross-lingual hate speech detection based on multilingual domain-specific word embeddings [4.769747792846004]
  We propose to address the problem of multilingual hate speech detection from the perspective of transfer learning. Our goal is to determine if knowledge from one particular language can be used to classify other language. We show that the use of our simple yet specific multilingual hate representations improves classification results.
  arXiv  Detail & Related papers  (2021-04-30T02:24:50Z)
• Leveraging cross-platform data to improve automated hate speech detection [0.0]
  Most existing approaches for hate speech detection focus on a single social media platform in isolation. Here we propose a new cross-platform approach to detect hate speech which leverages multiple datasets and classification models from different platforms. We demonstrate how this approach outperforms existing models, and achieves good performance when tested on messages from novel social media platforms.
  arXiv  Detail & Related papers  (2021-02-09T15:52:34Z)
• Detecting Online Hate Speech: Approaches Using Weak Supervision and Network Embedding Models [2.3322477552758234]
  We propose a weak supervision deep learning model that quantitatively uncover hateful users and (ii) present a novel qualitative analysis to uncover indirect hateful conversations. We evaluate our model on 19.2M posts and show that our weak supervision model outperforms the baseline models in identifying indirect hateful interactions. We also analyze a multilayer network, constructed from two types of user interactions in Gab(quote and reply) and interaction scores from the weak supervision model as edge weights, to predict hateful users.
  arXiv  Detail & Related papers  (2020-07-24T18:13:52Z)

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.