RETRACTED ARTICLE: Multilingual hate speech detection sentimental analysis on social media platforms using optimal feature extraction and hybrid diagonal gated recurrent neural network

Kar, Purbani; Debbarma, Swapan

doi:10.1007/s11227-023-05361-6

Cited by 2 publications

(2 citation statements)

References 33 publications

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“…Adding to that, many BERT-based data augmentation methods are successfully incorporated to generate more data in various languages as illustrates in Takawane et al (2023) where researchers, here, managed to enhance these models’ performance on Code-Mixed Hindi-English hate speech data. Moreover, Kar & Debbarma (2023) explored a system using hybrid diagonal gated recurrent neural networks (DGRNN) within an optimal feature extraction technique in multilingual code-mixed texts in English, Hindi, and German. Also, Das, Pandey & Mukherjee (2023) emphasized both the strengths and limitations of the ChatGPT model in hate speech detection in eleven languages.…”

Section: Approaches On Multilingual Hate Speech Detectionmentioning

confidence: 99%

A survey on multi-lingual offensive language detection

Mnassri,

Farahbakhsh,

Chalehchaleh

et al. 2024

PeerJ Computer Science

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The prevalence of offensive content on online communication and social media platforms is growing more and more common, which makes its detection difficult, especially in multilingual settings. The term “Offensive Language” encompasses a wide range of expressions, including various forms of hate speech and aggressive content. Therefore, exploring multilingual offensive content, that goes beyond a single language, focus and represents more linguistic diversities and cultural factors. By exploring multilingual offensive content, we can broaden our understanding and effectively combat the widespread global impact of offensive language. This survey examines the existing state of multilingual offensive language detection, including a comprehensive analysis on previous multilingual approaches, and existing datasets, as well as provides resources in the field. We also explore the related community challenges on this task, which include technical, cultural, and linguistic ones, as well as their limitations. Furthermore, in this survey we propose several potential future directions toward more efficient solutions for multilingual offensive language detection, enabling safer digital communication environment worldwide.

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Section: Approaches On Multilingual Hate Speech Detectionmentioning

confidence: 99%

A survey on multi-lingual offensive language detection

Mnassri,

Farahbakhsh,

Chalehchaleh

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Automatic identification of offensive language allows the platform to detect offensive language and remove it faster and more efficiently than manual filtering, which is very time-consuming. Therefore, the task of automatic offensive language detection [6,7] has been widely discussed by researchers.…”

Section: Introductionmentioning

confidence: 99%

RB_BG_MHA: A RoBERTa-Based Model with Bi-GRU and Multi-Head Attention for Chinese Offensive Language Detection in Social Media

Xu,

Liu

2023

Applied Sciences

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Offensive language in social media affects the social experience of individuals and groups and hurts social harmony and moral values. Therefore, in recent years, the problem of offensive language detection has attracted the attention of many researchers. However, the primary research currently focuses on detecting English offensive language, while few studies on the Chinese language exist. In this paper, we propose an innovative approach to detect Chinese offensive language. First, unlike previous approaches, we utilized both RoBERTa’s sentence-level and word-level embedding, combining the sentence embedding and word embedding of RoBERTa’s model, bidirectional GRU, and multi-head self-attention mechanism. This feature fusion allows the model to consider sentence-level and word-level semantic information at the same time so as to capture the semantic information of Chinese text more comprehensively. Second, by concatenating the output results of multi-head attention with RoBERTa’s sentence embedding, we achieved an efficient fusion of local and global information and improved the representation ability of the model. The experiments showed that the proposed model achieved 82.931% accuracy and 82.842% F1-score in Chinese offensive language detection tasks, delivering high performance and broad application potential.

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RETRACTED ARTICLE: Multilingual hate speech detection sentimental analysis on social media platforms using optimal feature extraction and hybrid diagonal gated recurrent neural network

Cited by 2 publications

References 33 publications

A survey on multi-lingual offensive language detection

A survey on multi-lingual offensive language detection

RB_BG_MHA: A RoBERTa-Based Model with Bi-GRU and Multi-Head Attention for Chinese Offensive Language Detection in Social Media

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