<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Cyberbullying has become a ubiquitous menace in our digitally connected society, requiring strong detection and classification systems. This study presents a multi-tiered system that reliably detects and classifies instances of cyberbullying on a variety of platforms by utilising cutting-edge machine learning and natural language processing approaches. Our algorithm, which was trained on a wide range of datasets, shows excellent accuracy in differentiating between instances of cyberbullying and non-bullying situations while taking linguistic and cultural quirks into account. Furthermore, our flexible system guarantees applicability by adjusting to changing cyberbullying patterns. By promoting safer and more inclusive digital communities, our research helps to design proactive treatments that lessen the effects of online harassment. This study introduces a robust multi-tiered system designed for the detection and classification of cyberbullying across diverse digital platforms. Leveraging state-of-the-art machine learning and natural language processing techniques, our algorithm, trained on extensive datasets, exhibits exceptional accuracy in distinguishing cyberbullying instances from non-bullying scenarios while accommodating linguistic and cultural nuances. The system’s adaptability to evolving cyberbullying patterns ensures continued efficacy. By fostering safer and more inclusive online environments, our research contributes to proactive measures and mitigates the impact of digital harassment.</p>
</abstract>

Cyberbullying has become a ubiquitous menace in our digitally connected society, requiring strong detection and classification systems. This study presents a multi-tiered system that reliably detects and classifies instances of cyberbullying on a variety of platforms by utilising cutting-edge machine learning and natural language processing approaches. Our algorithm, which was trained on a wide range of datasets, shows excellent accuracy in differentiating between instances of cyberbullying and non-bullying situations while taking linguistic and cultural quirks into account. Furthermore, our flexible system guarantees applicability by adjusting to changing cyberbullying patterns. By promoting safer and more inclusive digital communities, our research helps to design proactive treatments that lessen the effects of online harassment. This study introduces a robust multi-tiered system designed for the detection and classification of cyberbullying across diverse digital platforms. Leveraging state-of-the-art machine learning and natural language processing techniques, our algorithm, trained on extensive datasets, exhibits exceptional accuracy in distinguishing cyberbullying instances from non-bullying scenarios while accommodating linguistic and cultural nuances. The system’s adaptability to evolving cyberbullying patterns ensures continued efficacy. By fostering safer and more inclusive online environments, our research contributes to proactive measures and mitigates the impact of digital harassment.

ML and Natural Language Processing : Cyberbullying Detection System for Safer and Culturally Adaptive Digital Communities

Journal of Smart Internet of Things

This work is licensed under the Creative Commons Attribution 4.0 International License.

Cyberbullying has become a ubiquitous menace in our digitally connected society, requiring strong detection and classification systems. This study presents a...

ML and Natural Language Processing : Cyberbullying Detection System for Safer and Culturally Adaptive Digital Communities

Publié en ligne: 15 déc. 2023

Pages: 193 - 205

Reçu: 17 août 2023

Accepté: 20 sept. 2023

DOI: https://doi.org/10.2478/jsiot-2023-0020

Mots clés
Cyberbullying, Machine Learning, Natural language processing, Social media

© 2023 Viraj Shah et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

ML and Natural Language Processing : Cyberbullying Detection System for Safer and Culturally Adaptive Digital Communities

Publié en ligne: 15 déc. 2023

Pages: 193 - 205

Reçu: 17 août 2023

Accepté: 20 sept. 2023

DOI: https://doi.org/10.2478/jsiot-2023-0020

Mots clésCyberbullying, Machine Learning, Natural language processing, Social media

© 2023 Viraj Shah et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Cyberbullying, Machine Learning, Natural language processing, Social media