FL-XGBTC: federated learning inspired with XG-boost tuned classifier for YouTube spam content detection
- Title
- FL-XGBTC: federated learning inspired with XG-boost tuned classifier for YouTube spam content detection
- Creator
- Sharma V.; Sinha A.; Alkhayyat A.; Agarwal A.; Nikitha P.; Ramkumar S.; Rathee T.; Bhargavi M.; Kumar N.
- Description
- 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. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2024.
- Source
- International Journal of System Assurance Engineering and Management, Vol-15, No. 10, pp. 4923-4946.
- Date
- 2024-01-01
- Publisher
- Springer
- Subject
- Character; Classifiers; Federated learning; Ham; Machine learning YouTube; NLP; Sentiment analysis; Spam
- Coverage
- Sharma V., Computer Science Department, Christ University, Bengaluru, India; Sinha A., Computer Science Department, ICFAI Tech School, ICFAI University, Ranchi, India; Alkhayyat A., College of Technical Engineering, The Islamic University, Najaf, Iraq; Agarwal A., Dept of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Tamil Nadu, Krishnankoil, Virudhunagar, India; Nikitha P., Dept of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Tamil Nadu, Krishnankoil, Virudhunagar, India; Ramkumar S., Dept of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Tamil Nadu, Krishnankoil, Virudhunagar, India; Rathee T., Department of Information Technology, Maharaja Surajmal Institute of Technology, New Delhi, India; Bhargavi M., Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, India; Kumar N., Lovely Professional University, PB, Phagwara, India
- Rights
- Restricted Access
- Relation
- ISSN: 9756809
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Sharma V.; Sinha A.; Alkhayyat A.; Agarwal A.; Nikitha P.; Ramkumar S.; Rathee T.; Bhargavi M.; Kumar N., “FL-XGBTC: federated learning inspired with XG-boost tuned classifier for YouTube spam content detection,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 26, 2025, https://archives.christuniversity.in/items/show/12838.