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

Vandana Sharma (), Anurag Sinha (), Ahmed Alkhayyat (), Ankit Agarwal (), Peddi Nikitha (), Sable Ramkumar (), Tripti Rathee (), Mopuru Bhargavi () and Nitish Kumar ()
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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

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 10, No 16, 4923-4946

Abstract: 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.

Keywords: Federated learning; NLP; Machine learning YouTube; Sentiment analysis; Classifiers; Ham; Spam; Character (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13198-024-02502-9

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