Detecting Kids Cyberbullying Using Transfer Learning Approach: Transformer Fine-Tuning Models

Yafooz, Wael M. S.; Al-Dhaqm, Arafat; Alsaeedi, Abdullah

doi:10.1007/978-3-031-21199-7_18

Cited by 12 publications

(5 citation statements)

References 35 publications

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“…Various studies have been reported that investigate and recognize cybercrime [22,[31][32][33][34][35][36][37][38][39][40], data cracks [23,[41][42][43][44][45][46][47][48][49][50], and other digital risks [24,25,[41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58].…”

Section: A Related Workmentioning

confidence: 99%

A Cybersecurity Awareness Model for the Protection of Saudi Students from Social Media Attacks

Alotibi

2024

Eng. Technol. Appl. Sci. Res.

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Social engineering addresses a broad category of techniques aiming to persuade someone to reveal data or perform actions for criminal purposes, such as disclosing personal information about a particular target. Cybersecurity awareness is required to raise people’s understanding of how these social engineering techniques are being used and so their capacity to exploit them. To accomplish this objective, primary focus is given to educating and training individuals on how to recognize such incidents and respond to them effectively. To protect people against social engineering threats, various cybersecurity models and approaches have been proposed. There are, however, a few differences between these models, since they are developed for specific purposes. Thus, the main objective of this study is to develop a cybersecurity awareness model specifically designed for Saudi students to protect them from social engineering attacks. The design science methodology was utilized in this study. The proposed model consists of four main stages: education and training, developing policies and guidelines, improving Saudi schools’ security, as well as monitoring and evaluation. The model introduced can ensure the safety and privacy of students, teachers, and staff across different social platforms.

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Section: A Related Workmentioning

confidence: 99%

A Cybersecurity Awareness Model for the Protection of Saudi Students from Social Media Attacks

Alotibi

2024

Eng. Technol. Appl. Sci. Res.

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“…The transformer model can capture intricate associations between words and contexts. BERT can accurately detect bullying behavior in social media messages when the cue is subtle or indirect [22,116,117].…”

Section: Bert (Bidirectional Encoder Representations From Transformers)mentioning

confidence: 99%

A Review on Deep-Learning-Based Cyberbullying Detection

et al. 2023

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Bullying is described as an undesirable behavior by others that harms an individual physically, mentally, or socially. Cyberbullying is a virtual form (e.g., textual or image) of bullying or harassment, also known as online bullying. Cyberbullying detection is a pressing need in today’s world, as the prevalence of cyberbullying is continually growing, resulting in mental health issues. Conventional machine learning models were previously used to identify cyberbullying. However, current research demonstrates that deep learning surpasses traditional machine learning algorithms in identifying cyberbullying for several reasons, including handling extensive data, efficiently classifying text and images, extracting features automatically through hidden layers, and many others. This paper reviews the existing surveys and identifies the gaps in those studies. We also present a deep-learning-based defense ecosystem for cyberbullying detection, including data representation techniques and different deep-learning-based models and frameworks. We have critically analyzed the existing DL-based cyberbullying detection techniques and identified their significant contributions and the future research directions they have presented. We have also summarized the datasets being used, including the DL architecture being used and the tasks that are accomplished for each dataset. Finally, several challenges faced by the existing researchers and the open issues to be addressed in the future have been presented.

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“…Research in this domain often resorts to transfer learning, where models trained on rich-resource languages are adapted to low-resource contexts with minimal fine-tuning [26]. Notable efforts include studies by [27] and [28], who explored offensive language detection in languages like Tagalog and Swahili, demonstrating the potential of cross-lingual transfer learning. Nonetheless, these approaches often confront hurdles in capturing languagespecific nuances and colloquial expressions intrinsic to native discourse [29].…”

Section: B Offensive Language Detection In Low Resourcementioning

confidence: 99%

Offensive Language Detection on Online Social Networks using Hybrid Deep Learning Architecture

Kazbekova,

Ismagulova,

Kemelbekova

et al. 2023

IJACSA

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In the digital era, online social networks (OSNs) have revolutionized communication, creating spaces for vibrant public discourse. However, these platforms also harbor offensive language that can proliferates hate speech, cyberbullying, and discrimination, significantly undermining the quality of online interactions and posing severe social implications. This research paper introduces a sophisticated approach to offensive language detection on OSNs, employing a novel Hybrid Deep Learning Architecture (HDLA). The urgency of addressing offensive content is juxtaposed with the challenges inherent in accurately identifying nuanced communications, thus necessitating an advanced model that transcends the limitations of traditional natural language processing techniques. The proposed HDLA model synergistically integrates Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks, capitalizing on the strengths of both methodologies. While the CNN component excels in the hierarchical extraction of spatial features within text data, identifying offensive patterns often concealed in the structural nuances, the LSTM network, adept in processing sequential data, captures the contextual dependencies in user posts over time. This duality ensures a comprehensive analysis of complex linguistic constructs, enhancing the detection accuracy for both overt and covert offensive content. Our research meticulously evaluates the HDLA model using extensive, multi-source datasets reflective of diverse OSN environments, establishing benchmarks against prevailing deep learning models. Results indicate a substantial improvement in precision, recall, and F1-score, demonstrating the model's efficacy in identifying offensive language amidst varying degrees of subtlety and complexity. Furthermore, the model maintains high interpretability, providing insights into the intricate mechanisms of offensive content propagation. Our findings underscore the potential of HDLA in fostering healthier online communities by efficiently curating digital content, thereby upholding the integrity of digital communication spaces.

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Detecting Kids Cyberbullying Using Transfer Learning Approach: Transformer Fine-Tuning Models

Cited by 12 publications

References 35 publications

A Cybersecurity Awareness Model for the Protection of Saudi Students from Social Media Attacks

A Cybersecurity Awareness Model for the Protection of Saudi Students from Social Media Attacks

A Review on Deep-Learning-Based Cyberbullying Detection

Offensive Language Detection on Online Social Networks using Hybrid Deep Learning Architecture

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