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FL-XGBTC: federated learning inspired with XG-boost tuned classifier for YouTube spam content detection

Author

Listed:
  • Vandana Sharma

    (Christ University)

  • Anurag Sinha

    (ICFAI University)

  • Ahmed Alkhayyat

    (The Islamic University)

  • Ankit Agarwal

    (Kalasalingam Academy of Research and Education)

  • Peddi Nikitha

    (Kalasalingam Academy of Research and Education)

  • Sable Ramkumar

    (Kalasalingam Academy of Research and Education)

  • Tripti Rathee

    (Maharaja Surajmal Institute of Technology)

  • Mopuru Bhargavi

    (Koneru Lakshmaiah Education Foundation)

  • Nitish Kumar

    (Lovely Professional University)

Abstract

The problem of spam content in YouTube comments is an ongoing issue, and detecting such content is a critical task to maintain the quality of user experience on the platform. In this study, we propose a Federated Learning Inspired XG-Boost Tuned Classifier, FL-XGBTC, for YouTube spam content detection. The proposed model leverages the advantages of federated learning, which enables the training of a model collaboratively across multiple devices without sharing raw data. The FL-XGBTC model is based on the XGBoost algorithm, which is a powerful and widely used ensemble learning algorithm for classification tasks. The proposed model was trained on a large and diverse dataset of YouTube comments, which includes both spam and non-spam comments. The results demonstrate that the FL-XGBTC model achieved a high level of accuracy in detecting spam content in YouTube comments, outperforming several baseline models. Additionally, the proposed model provides the benefit of preserving user privacy, which is a critical consideration in modern machine-learning applications. Overall, the proposed Federated Learning Inspired XG-Boost Tuned Classifier provides a promising solution for YouTube spam content detection that leverages the benefits of federated learning and ensemble learning algorithms. The major contribution of this work is to demonstrate and propose a framework for showing a distributed federated classifier for the multiscale classification of youtube spam comments using the Ensemble learning method.

Suggested Citation

  • Vandana Sharma & Anurag Sinha & Ahmed Alkhayyat & Ankit Agarwal & Peddi Nikitha & Sable Ramkumar & Tripti Rathee & Mopuru Bhargavi & Nitish Kumar, 2024. "FL-XGBTC: federated learning inspired with XG-boost tuned classifier for YouTube spam content detection," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(10), pages 4923-4946, October.
  • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:10:d:10.1007_s13198-024-02502-9
    DOI: 10.1007/s13198-024-02502-9
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    References listed on IDEAS

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    1. Noha Alnazzawi & Najlaa Alsaedi & Fahad Alharbi & Najla Alaswad, 2022. "Using Social Media to Detect Fake News Information Related to Product Marketing: The FakeAds Corpus," Data, MDPI, vol. 7(4), pages 1-13, April.
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