Cite this DOI

Sanjeevini S.H, S. S. (2024). BULLY NET: UNMASKING CYBER BULLIES ON SOCIAL NETWORKS. *Journal of Science & Technology*, *9*(1), 61–75. https://doi.org/10.46243/jst.2024.v9.i1.pp61-75

@article{sanjeevinish2024bully,
  author    = {Sanjeevini S.H, Sanjeevini S.H},
  title     = {{BULLY NET: UNMASKING CYBER BULLIES ON SOCIAL NETWORKS}},
  journal   = {Journal of Science \& Technology},
  year      = {2024},
  month     = {jan},
  volume    = {9},
  number    = {1},
  pages     = {61--75},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2024.v9.i1.pp61-75},
  url       = {https://doi.org/10.46243/jst.2024.v9.i1.pp61-75},
  language  = {en},
  abstract  = {In the rapidly evolving landscape of online communication, the surge in cyberbullying has emerged as a critical challenge, necessitating innovative solutions for detection and prevention. Existing approaches often reply on simplistic keyword-based filters or rule-based methods, struggling to keep pace with the dynamic nature of cyberbullying scenarios. The intricate and varied nature of online harassment demands a more sophisticated system capable of discerning subtle nuances within social media interactions. Recognizing this gap, the proposed BullyNet system introduces a character-level convolutional neural network (CNN) approach to enhance the accuracy and adaptability of cyberbullying detection. By incorporating both word-based and character-based models, BullyNet aims to provide a holistic understanding of language expression and contextual cues, offering a nuanced solution to the complex challenges posed by cyberbullying. This system’s multifaceted approach, encompassing preprocessing, training, and evaluation of CNN models, is designed to address the shortcomings of existing systems and contribute to the creation of a safer online environment. BullyNet stands as a promising stride towards unmasking cyberbullies on social networks, emphasizing the need for advanced tools capable of navigating the intricate landscape of digital communication}
}

TY  - JOUR
TI  - BULLY NET: UNMASKING CYBER BULLIES ON SOCIAL NETWORKS
AU  - Sanjeevini S.H, Sanjeevini S.H
JO  - Journal of Science & Technology
PY  - 2024
DA  - 2024/01/25/
VL  - 9
IS  - 1
SP  - 61
EP  - 75
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - In the rapidly evolving landscape of online communication, the surge in cyberbullying has emerged as a critical challenge, necessitating innovative solutions for detection and prevention. Existing approaches often reply on simplistic keyword-based filters or rule-based methods, struggling to keep pace with the dynamic nature of cyberbullying scenarios. The intricate and varied nature of online harassment demands a more sophisticated system capable of discerning subtle nuances within social media interactions. Recognizing this gap, the proposed BullyNet system introduces a character-level convolutional neural network (CNN) approach to enhance the accuracy and adaptability of cyberbullying detection. By incorporating both word-based and character-based models, BullyNet aims to provide a holistic understanding of language expression and contextual cues, offering a nuanced solution to the complex challenges posed by cyberbullying. This system’s multifaceted approach, encompassing preprocessing, training, and evaluation of CNN models, is designed to address the shortcomings of existing systems and contribute to the creation of a safer online environment. BullyNet stands as a promising stride towards unmasking cyberbullies on social networks, emphasizing the need for advanced tools capable of navigating the intricate landscape of digital communication
DO  - 10.46243/jst.2024.v9.i1.pp61-75
UR  - https://doi.org/10.46243/jst.2024.v9.i1.pp61-75
ER  -

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    "title": "BULLY NET: UNMASKING CYBER BULLIES ON SOCIAL NETWORKS",
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    "abstract": "In the rapidly evolving landscape of online communication, the surge in cyberbullying has emerged as a critical challenge, necessitating innovative solutions for detection and prevention. Existing approaches often reply on simplistic keyword-based filters or rule-based methods, struggling to keep pace with the dynamic nature of cyberbullying scenarios. The intricate and varied nature of online harassment demands a more sophisticated system capable of discerning subtle nuances within social media interactions. Recognizing this gap, the proposed BullyNet system introduces a character-level convolutional neural network (CNN) approach to enhance the accuracy and adaptability of cyberbullying detection. By incorporating both word-based and character-based models, BullyNet aims to provide a holistic understanding of language expression and contextual cues, offering a nuanced solution to the complex challenges posed by cyberbullying. This system’s multifaceted approach, encompassing preprocessing, training, and evaluation of CNN models, is designed to address the shortcomings of existing systems and contribute to the creation of a safer online environment. BullyNet stands as a promising stride towards unmasking cyberbullies on social networks, emphasizing the need for advanced tools capable of navigating the intricate landscape of digital communication",
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2024.v9.i1.pp61-75 gives all four in one JSON answer.