Kernel Metadata Declaration — on demand

What the DOI identifies

BULLY NET: UNMASKING CYBER BULLIES ON SOCIAL NETWORKS

10.46243/jst.2024.v9.i1.pp61-75 · JournalArticle — an article in a journal · Digital · Visual · Language · en

Published 2024-01-25

Part of Journal of Science & Technology · ISSN 2456-5660 · vol. 9 · no. 1 · pp. 61–75

Principal agents

• Sanjeevini S.H Sanjeevini S.H (author → Author)
• Longman Publishers (publisher → Publisher)

Also in the record, outside the Kernel: the abstract, the licence, 4 links, 20 references. Source: Crossref (member 25296), registered 2024-02-16, last deposited 2026-09-17.

⬇ Record (JSON) ⬇ Declaration (XML)

System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1

Each element by the standard's name and the Handbook's (in grey), read off the record.

The record

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        "value": "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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            "unstructured": "Hinduja S, Patchin JW. Bullying, cyberbullying, and suicide. Arch Suicide Res. 2010;14(3):206-"
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            "unstructured": "Sugandhi R, Pande A, Chawla S, Agrawal A, Bhagat H. Methods for detection of cyberbullying: A survey. Paper presented at: 15th International Conference on Intelligent Systems Design and Applications; 2015; Marrakech, Morocco"
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            "key": "ref5",
            "unstructured": "Baldwin T, Cook P, Lui M, MacKinlay A, Wang L. How noisy social media text, how different social media sources. Paper presented at: 6th International Joint Conference on Natural Language Processing; 2013; Nagoya, Japan"
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            "key": "ref6",
            "unstructured": "Xu JM, Jun KS, Zhu X, Bellmore A. Learning from bullying traces in social media. Paper presented at: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies; 2012; Montreal, Canada"
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            "key": "ref7",
            "doi": "10.1145/2517312.2517314",
            "unstructured": "Freeman DM. Using naive Bayes to detect spammy names in social networks. Paper presented at: ACM Workshop on Artificial Intelligence and Security; 2013; Berlin, Germany"
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            "key": "ref8",
            "doi": "10.1109/icmla.2011.152",
            "unstructured": "Reynolds K, Kontostathis A, Edwards L. Using machine learning to detect cyberbullying. Paper presented at: 10th International Conference DOI:https://doi.org/10.46243/jst.2024.v9.i1.pp61-7546 Sanjeevini S.H, T. Pavani, P. Keerthana, A. Rachana: BULL Y NET: UNMASKING CYBER BULLIES ON SOCIAL NETWORKS on Machine learning and Applications and Workshops; 2011; Honolulu, HI"
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            "unstructured": "Kasture AS. A predictive model to detect online cyberbullying [master’s thesis]. Auckland, New Zealand: Auckland University of Technology; 2015"
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            "doi": "10.1007/978-3-642-36973-5_62",
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            "unstructured": "Dinakar K, Reichart R, Lieberman H. Modeling the detection of textual cyberbullying. Paper presented at: 5th International AAAI Conference on Weblogs and Social Media; 2011; Barcelona, Spain"
        },
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            "doi": "10.1109/socialcom-passat.2012.55",
            "unstructured": "Ying C, Zhou Y, Zhu S, Xu H. Detecting offensive language in social media to protect adolescent online safety. Paper presented at: 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Conference on Social Computing; 2012; Amsterdam, Netherlands"
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            "key": "ref13",
            "doi": "10.1109/taffc.2016.2531682",
            "unstructured": "Zhao R, Mao K. Cyberbullying detection based on semantic-enhanced marginalized denoising auto-encoder. IEEE Trans Affect Comput. 2017;8(3):328-339"
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            "doi": "10.1109/iccv.2017.324",
            "unstructured": "Lin TY, Goyal P, Girshick R, He K, Dollar P. Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell. 2017;99:2999-3007"
        },
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            "key": "ref15",
            "doi": "10.1177/1541204006286288",
            "unstructured": "Patchin JW, Hinduja S. Bullies move beyond the schoolyard a preliminary look at cyberbullying. Youth Violence Juvenile Justice. 2006;4(2):148-"
        },
        {
            "key": "ref16",
            "doi": "10.1111/j.1467-9450.2007.00611.x",
            "unstructured": "Robert S, Smith PK. Cyberbullying: another main type of bullying? Scand J Psychol. 2008;49(2):147-154"
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            "unstructured": "Smith PK, Jess M, Manuel C, Sonja F, Shanette R, Neil T. Cyberbullying: its nature and impact in secondary school pupils. J Child Psychol Psychiatry. 2008;49(4):376-385"
        },
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            "key": "ref18",
            "doi": "10.1016/j.chb.2009.11.014",
            "unstructured": "Tokunaga RS. Following you home from school: a critical review and synthesis of research on cyberbullying victimization. Comput Hum Behav. 2010;26(3):277-287"
        },
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            "key": "ref19",
            "unstructured": "Nahar V, Xue L, Pang C. An effective approach for cyberbullying detection. Commun Inf Sci Manag Eng. 2013;3(5):238-247"
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            "unstructured": "Kontostathis A, Reynolds K, Garron A, Edwards L. Detecting cyberbullying: Query terms and techniques. Paper presented at: 5th Annual ACM Web Science Conference; 2013; Paris, France"
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The Kernel Metadata Declaration

DOIMetadataKernel.xsd · namespace http://www.doi.org/2010/DOISchema · Attribute Value Sets 2.3 · draft — the agency's DOI name is filled in on accreditation

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