Hate Speech detection in the Bengali language: A dataset and its baseline evaluation

Romim, Nauros; Ahmed, Mosahed; Talukder, Hriteshwar; Islam, Md. Saiful

doi:10.48550/arxiv.2012.09686

Cited by 3 publications

(4 citation statements)

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“…Nevertheless, with the advances in multilingual parsers and deep learning technology, together with increasing pressures from policy-makers to handle hate speech issues at local resources, non-English HS detection toolkits have seen a steady increase. The figure indicates that about 51% of all works in this field are performed on English dataset, with an increase of proportion of other languages as well where Arabic (13% ) [93,59,12,143], Turkish (6%) [143,104], Greek (4%) [143,6,136], Danish (5%) [106,143], Hindi (4%) [121,22,88], German (4% ) [72,120], Malayalam (3%) [130,109], Tamil (3%) [130,20], Chinese (1%) [138,139,155], Italian (2%) [116], Urdu (1%) [126,95,7], Russian(1%) [17], Bengali (1% ) [62,127,69], Korean (1%) [91], French (1%) [16,102,50], Indonesian (1%) [14], Portuguese (1%) [14], Spanish (1%) [56] and Polish (1%) [118] seem to dominate the rest of the languages in this field.…”

Section: Statistical Trends Of Resultsmentioning

confidence: 99%

A systematic review of Hate Speech automatic detection using Natural Language Processing

Saroar¹,

Oussalah²

2021

Preprint

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With the multiplication of social media platforms, which offer anonymity, easy access and online community formation and online debate, the issue of hate speech detection and tracking becomes a growing challenge to society, individual, policy-makers and researchers. Despite efforts for leveraging automatic techniques for automatic detection and monitoring, their performances are still far from satisfactory, which constantly calls for future research on the issue. This paper provides a systematic review of literature in this field, with a focus on natural language processing and deep learning technologies, highlighting the terminology, processing pipeline, core methods employed, with a focal point on deep learning architecture. From a methodological perspective, we adopt PRISMA guideline of systematic review of the last 10 years literature from ACM Digital Library and Google Scholar. In the sequel, existing surveys, limitations, and future research directions are extensively discussed.

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Section: Statistical Trends Of Resultsmentioning

confidence: 99%

A systematic review of Hate Speech automatic detection using Natural Language Processing

Saroar¹,

Oussalah²

2021

Preprint

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“…As for the Bengali dataset, we reuse the one from Romim et al [19]. The authors extracted comments from Facebook pages and Youtube videos whose topics range from celebrity and sports to crime and politics.…”

Section: Datamentioning

confidence: 99%

An exploratory experiment on Hindi, Bengali hate-speech detection and transfer learning using neural networks

Phung¹,

Cloos²

2022

Preprint

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This work presents our approach to train a neural network to detect hate-speech texts in Hindi and Bengali. We also explore how transfer learning can be applied to learning these languages, given that they have the same origin and thus, are similar to some extend. Even though the whole experiment was conducted with low computational power, the obtained result is comparable to the results of other, more expensive, models. Furthermore, since the training data in use is relatively small and the two languages are almost entirely unknown to us, this work can be generalized as an effort to demystify lost or alien languages that no human is capable of understanding.

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“…Accurate identification of hate speech in Bengali is a challenging task. Only a few restrictive approaches [24,25,2] have been proposed so far. Romim et al [24] prepared a dataset of 30K comments, making it one of the largest datasets for identifying offensive and hateful statements.…”

Section: Related Workmentioning

confidence: 99%

“…Only a few restrictive approaches [24,25,2] have been proposed so far. Romim et al [24] prepared a dataset of 30K comments, making it one of the largest datasets for identifying offensive and hateful statements. However, this dataset has several issues.…”

Section: Related Workmentioning

confidence: 99%

Multimodal Hate Speech Detection from Bengali Memes and Texts

Karim¹,

Dey²,

Islam³

et al. 2022

Preprint

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Numerous works have been proposed to employ machine learning (ML) and deep learning (DL) techniques to utilize textual data from social media for anti-social behavior analysis such as cyberbullying, fake news propagation, and hate speech mainly for highlyresourced languages like English. However, despite of having lot of diversity and millions of native speakers, some languages such as Bengali are under-resourced, which is due to lack of computational resources for natural language processing (NLP). Like English, Bengali social media content also include images along with texts (e.g., multimodal contents are posted by embedding short texts into images on Facebook), only the textual data is not enough to judge them (e.g., to determine they are hate speech). In those cases, images might give extra context to make a proper judgement. This paper is about hate speech detection from multimodal Bengali memes and texts. We prepared the only multimodal hate speech detection dataset 1 for-a-kind of problem for Bengali. We train several neural architectures (i.e., neural networks like Bi-LSTM/Conv-LSTM with word embeddings, EfficientNet + transformer architectures such as monolingual Bangla BERT, multilingual BERT-cased/uncased, and XLM-RoBERTa) that jointly analyze textual and visual information for hate speech detection. The Conv-LSTM and XLM-RoBERTa models performed best for texts, yielding F1 scores of 0.78 and 0.82, respectively. As of memes, ResNet152 and DenseNet201 models yield F1 scores of 0.78 and 0.7, respectively. The multimodal fusion of mBERT-uncased + EfficientNet-B1 performed the best, yielding an F1 score of 0.80. Our study suggests that memes are moderately useful for hate speech detection in Bengali, but none of multimodal models outperform unimodal models analyzing only textual data. Further, to foster reproducible research, we plan to make available datasets, source codes, models, and notebooks 2 .

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Hate Speech detection in the Bengali language: A dataset and its baseline evaluation

Cited by 3 publications

References 0 publications

A systematic review of Hate Speech automatic detection using Natural Language Processing

A systematic review of Hate Speech automatic detection using Natural Language Processing

An exploratory experiment on Hindi, Bengali hate-speech detection and transfer learning using neural networks

Multimodal Hate Speech Detection from Bengali Memes and Texts

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