HateGAN: Adversarial Generative-Based Data Augmentation for Hate Speech Detection

Cao, Rui; Lee, Roy Ka-Wei

doi:10.18653/v1/2020.coling-main.557

Cited by 30 publications

(11 citation statements)

References 36 publications

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“…This synonym is usually gotten from a dictionary or thesaurus such as WordNet in [12] or calculated using the cosine similarities between the target word and words in a pre-trained word embedding such as Word2Vec in [30,39] or Glove in [12]. Another well-known method is generation of new text using RNN [30] or GPT [12,18,36,42] or GAN [3]. In [44], they applied dependency based embeddings for word substitution to generate text while leveraging textual membership queries.…”

Section: Related Studiesmentioning

confidence: 99%

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Token replacement-based data augmentation methods for hate speech detection

2022

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Hate speech detection mostly involves the use of text data. This data, usually sourced from various social media platforms, have been known to be plagued with numerous issues that result in a reduction of its quality and hence, the quality of the trained models. Some of these issues are the lack of diversity and the diminutive class of interest in the dataset which results in overfitted models that do not generalize well on other or newly collected data. The different ways of handling these issues include augmenting the data with diverse samples, engineering non-redundant features or designing robust classification models. In this study, the focus is on the data augmentation aspect. Data augmentation is a popular method for improving the quality of existing datasets by generating synthetic samples that mimic the distribution of the original samples. There is a lack of extensive studies on how hate speech texts respond to varying textual data augmentation techniques and methods. Specifically, we provide further insight into the token replacement method of textual data augmentation by performing empirical studies that investigate which embedding method(s) is a robust source of synonym for replacement process, what effective method(s) can be used to select words to be replaced, and how to confirm if the label within each class is preserved. Our proposed methods, validated on two commonly used hate speech datasets affected by a known lack of diversity and diminutive class of interest issues, significantly improve classification performance and provides insights into token replacement methods.

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Section: Related Studiesmentioning

confidence: 99%

“…3 : This contains approximately 25k instances from Twitter in English with labels of Hate, Offensive, or Neither hateful nor offensive[6], more specifically, 5.77%…”

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confidence: 99%

Token replacement-based data augmentation methods for hate speech detection

2022

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“…As a type of semi-supervised method, GANs include the generative model, which is mainly used to challenge the discriminator of GANs, while the generative models in some DA methods are directly used to augment training data. Moreover, the generative model of GANS is applied as a DA method in some scenes like [61,119,96,68,109,116], and have demonstrated to be effective for data augmentation purposes.…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

Data Augmentation Approaches in Natural Language Processing: A Survey

Li,

Hou,

Che

2021

Preprint

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As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where deep learning techniques may fail. It is widely applied in computer vision then introduced to natural language processing and achieves improvements in many tasks. One of the main focuses of the DA methods is to improve the diversity of training data, thereby helping the model to better generalize to unseen testing data. In this survey, we frame DA methods into three categories based on the diversity of augmented data, including paraphrasing, noising, and sampling. Our paper sets out to analyze DA methods in detail according to the above categories. Further, we also introduce their applications in NLP tasks as well as the challenges.

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“…The increase in the use of social media outlets has led to exploitation in HS organizations that advocate or criticize hate and offensive materials. Every other day, social media giants continue their efforts to detect and prevent the spread of offensive, anarchical, or propagation HS materials without slowing down [2]. In the face of crises such as COVID-19, which unexpectedly broke out on the world agenda, technology giants had to keep their existing technological structures up-to-date to prevent the spread of sharing HS materials.…”

Section: Introductionmentioning

confidence: 99%

Deep-Cov19-Hate: A Textual-Based Novel Approach for Automatic Detection of Hate Speech in Online Social Networks throughout COVID-19 with Shallow and Deep Learning Models

Baydogan

Alataş

2022

Teh. vjesn.

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The use of various online social media platforms rising day by day caused an increase in the correct or incorrect information shared by users, especially during COVID-19. The introduction of COVID-19 on the world agenda gave rise to an overall bad reaction against East Asia (esp. China) in online social media platforms. The social media users who spread degrading, racist, disrespectful, abusive, discriminatory, critical, abuse, harsh, offensive, etc. posts accused the Asian people of being responsible for the outbreak of COVID-19. For this reason, the development of the Hate Speech Detection (HSD) system was necessary in order to prevent the spread of these posts about COVID-19. In this article, a textual-based study on COVID-19-related hate speech (HS) sharing in online social networks was carried out with Shallow Learning (SL) and Deep Learning (DL) methods. In the first step of this study, typical Natural Language Processing (NLP) pipeline was applied for gathered two different datasets. This NLP pipeline was performed using bag of words, term frequency, document matrix, etc. techniques for features extraction representing datasets. Then, ten different SL and DL models were fine-tuned for HS datasets related to COVID-19. Accuracy, precision, sensitivity, and F-score performance measurement criteria were calculated to compare the performance of the SL and DL algorithms for the problem of HSD. The RNN, one of the models proposed for the first and second dataset in HSD, prevailed with the highest accuracy values of 78.7% and 90.3%, respectively. Due to the promising results of all approaches operated in the HSD, they are forecasted to be chosen in the solution of many other social media and network problems related to COVID-19.

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HateGAN: Adversarial Generative-Based Data Augmentation for Hate Speech Detection

Cited by 30 publications

References 36 publications

Token replacement-based data augmentation methods for hate speech detection

Token replacement-based data augmentation methods for hate speech detection

Data Augmentation Approaches in Natural Language Processing: A Survey

Deep-Cov19-Hate: A Textual-Based Novel Approach for Automatic Detection of Hate Speech in Online Social Networks throughout COVID-19 with Shallow and Deep Learning Models

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