Hate Speech Detection Using Cross-Platform Social Media Data In English and German Language

• URL: http://arxiv.org/abs/2410.05287v1
• Date: Wed, 2 Oct 2024 10:22:53 GMT
• Title: Hate Speech Detection Using Cross-Platform Social Media Data In English and German Language
• Authors: Gautam Kishore Shahi, Tim A. Majchrzak,
• Abstract summary: 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.
• Score: 6.200058263544999
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hate speech has grown into a pervasive phenomenon, intensifying during times of crisis, elections, and social unrest. Multiple approaches have been developed to detect hate speech using artificial intelligence, but a generalized model is yet unaccomplished. The challenge for hate speech detection as text classification is the cost of obtaining high-quality training data. This study focuses on detecting bilingual hate speech in YouTube comments and measuring the impact of using additional data from other platforms in the performance of the classification model. We examine the value of additional training datasets from cross-platforms for improving the performance of classification models. We also included factors such as content similarity, definition similarity, and common hate words to measure the impact of datasets on performance. Our findings show that adding more similar datasets based on content similarity, hate words, and definitions improves the performance of classification models. 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.

Related papers

• A Target-Aware Analysis of Data Augmentation for Hate Speech Detection [3.858155067958448]
  Hate speech is one of the main threats posed by the widespread use of social networks. We investigate the possibility of augmenting existing data with generative language models, reducing target imbalance. For some hate categories such as origin, religion, and disability, hate speech classification using augmented data for training improves by more than 10% F1 over the no augmentation baseline.
  arXiv  Detail & Related papers  (2024-10-10T15:46:27Z)
• Hate Speech Detection in Limited Data Contexts using Synthetic Data Generation [1.9506923346234724]
  We propose a data augmentation approach that addresses the problem of lack of data for online hate speech detection in limited data contexts. We present three methods to synthesize new examples of hate speech data in a target language that retains the hate sentiment in the original examples but transfers the hate targets. Our findings show that a model trained on synthetic data performs comparably to, and in some cases outperforms, a model trained only on the samples available in the target domain.
  arXiv  Detail & Related papers  (2023-10-04T15:10:06Z)
• Models See Hallucinations: Evaluating the Factuality in Video Captioning [57.85548187177109]
  We conduct a human evaluation of the factuality in video captioning and collect two annotated factuality datasets. We find that 57.0% of the model-generated sentences have factual errors, indicating it is a severe problem in this field. We propose a weakly-supervised, model-based factuality metric FactVC, which outperforms previous metrics on factuality evaluation of video captioning.
  arXiv  Detail & Related papers  (2023-03-06T08:32:50Z)
• Learning a Grammar Inducer from Massive Uncurated Instructional Videos [118.7279072358029]
  Video-aided grammar induction aims to leverage video information for finding more accurate syntactic grammars for accompanying text. We build a new model that can better learn video-span correlation without manually designed features. Our model yields higher F1 scores than the previous state-of-the-art systems trained on in-domain data.
  arXiv  Detail & Related papers  (2022-10-22T00:22:55Z)
• 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)
• Statistical Analysis of Perspective Scores on Hate Speech Detection [7.447951461558536]
  State-of-the-art hate speech classifiers are efficient only when tested on the data with the same feature distribution as training data. In such a diverse data distribution relying on low level features is the main cause of deficiency due to natural bias in data. We show that, different hate speech datasets are very similar when it comes to extract their Perspective Scores.
  arXiv  Detail & Related papers  (2021-06-22T17:17:35Z)
• Sentiment analysis in tweets: an assessment study from classical to modern text representation models [59.107260266206445]
  Short texts published on Twitter have earned significant attention as a rich source of information. Their inherent characteristics, such as the informal, and noisy linguistic style, remain challenging to many natural language processing (NLP) tasks. This study fulfils an assessment of existing language models in distinguishing the sentiment expressed in tweets by using a rich collection of 22 datasets.
  arXiv  Detail & Related papers  (2021-05-29T21:05:28Z)
• 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)
• Trawling for Trolling: A Dataset [56.1778095945542]
  We present a dataset that models trolling as a subcategory of offensive content. The dataset has 12,490 samples, split across 5 classes; Normal, Profanity, Trolling, Derogatory and Hate Speech.
  arXiv  Detail & Related papers  (2020-08-02T17:23:55Z)

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.