Detection of Bangla Hate Comments and Cyberbullying in Social Media Using NLP and Transformer Models

Emon, Md. Imdadul Haque; Iqbal, Khondoker Nazia; Mehedi, Md Humaion Kabir; Mahbub, Mohammed Julfikar Ali; Rasel, Annajiat Alim

doi:10.1007/978-3-031-12638-3_8

Cited by 16 publications

(4 citation statements)

References 7 publications

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“…Bengali-language cyberbullying on social media [27]. On a dataset of 44,001 Bangla comments from Facebook, they used a variety of transformer models, including Bangla Bidirectional Encoder Representations from Transformers (BERT), Bengali DistilBERT, and Cross-lingual Language Model (XLM-RoBERTa).…”

Section: Emon Et Al In 2022 Suggested a Model For Locatingmentioning

confidence: 99%

A Review of the Past Decade's Research Outcomes on Classifying Cyberbullying

Haider Ameen,

Saad Al-Mukhtar,

Qahtan Ahmed

et al. 2024

ANJS

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Cyberbullying is a new form of online violence that has emerged as a result of the social media industry's explosive expansion since it allows for indirect communication. Despite the usefulness of digital media, it has been used by weak people to threaten and bully others online. In the last ten years, research has shown that children and teenagers are increasingly experiencing cyberbullying as a concern. This paper examines the research conducted during the previous ten years, categorizes it, and presents statistical analyses of the data collected during that time. A table is used to present various data, including the dataset that was used, its size (number of samples, posts, or messages), the methods that were employed, and the metrics that were gathered from the examined research that was taken from the publications that were looked into. This survey will be helpful to everyone who wants to advance their understanding of how machine learning may be used to identify cyberbullying, and it may help create a social media environment that is safe and relatively healthy by automatically identifying bullying communications.

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Section: Emon Et Al In 2022 Suggested a Model For Locatingmentioning

confidence: 99%

A Review of the Past Decade's Research Outcomes on Classifying Cyberbullying

Haider Ameen,

Saad Al-Mukhtar,

Qahtan Ahmed

et al. 2024

ANJS

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“…Most previous studies focus on research done using social media data. In particular, tweets, and comments that are short and lack a sequence of events are preferred to exploit, especially in the field of detecting abuse [2,8,11,17].…”

Section: Inputmentioning

confidence: 99%

“…In recent years, abusive language detection and other related problems such as offensive language have attracted much attention from the Natural Language Processing (NLP) community. Research in the field has largely considered some particular topics such as HateSpeech [8,26], Cyberbullying [1][2][3]11], and Sexism/Racism [6,16,37]. By and large, these works have been conducted in English or other resource-rich languages, including Chinese, Spain, and French.…”

Section: Related Workmentioning

confidence: 99%

“…By and large, these works have been conducted in English or other resource-rich languages, including Chinese, Spain, and French. Besides, the majority of datasets for these tasks have been collected from two main popular social media platforms these days, consisting of Twitter [2,8], and Facebook [11,17]. Similarly, in the Vietnamese domain, most of the works related to the field have also mainly focused on Hate Speech such as ViHSD [21], HSD-VLSP [36], UIT-ViCTSD [24], and Vi-HOS [14] based on comments crawling from social media platforms.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Abusive Span Detection for Vietnamese Narrative Texts

Nguyen,

Thi-Kim Phan,

Nguyen

et al. 2023

Proceedings of the 12th International Symposium on Information and Communication Technology

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Abuse in its various forms, including physical, psychological, verbal, sexual, financial, and cultural, has a negative impact on mental health. However, there are limited studies on applying natural language processing (NLP) in this field in Vietnam. Therefore, we aim to contribute by building a human-annotated Vietnamese dataset for detecting abusive content in Vietnamese narrative texts. We sourced these texts from VnExpress, Vietnam's popular online newspaper, where readers often share stories containing abusive content. Identifying and categorizing abusive spans in these texts posed significant challenges during dataset creation, but it also motivated our research. We experimented with lightweight baseline models by freezing PhoBERT and XLM-RoBERTa and using their hidden states in a BiLSTM to assess the complexity of the dataset. According to our experimental results, PhoBERT outperforms other models in both labeled and unlabeled abusive span detection tasks. These results indicate that it has the potential for future improvements. CCS CONCEPTS• Computing methodologies → Natural language processing.

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Multi-feature Transformer for Multiclass Cyberbullying Detection in Bangla

Wahid

Imran

2023

IFIP Advances in Information and Communication Technology

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Detection of Bangla Hate Comments and Cyberbullying in Social Media Using NLP and Transformer Models

Cited by 16 publications

References 7 publications

A Review of the Past Decade's Research Outcomes on Classifying Cyberbullying

A Review of the Past Decade's Research Outcomes on Classifying Cyberbullying

Abusive Span Detection for Vietnamese Narrative Texts

Multi-feature Transformer for Multiclass Cyberbullying Detection in Bangla

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