Twitter spam drift detection by semi supervised learning approach using YATSI algorithm
- 12 August 2024
- journal article
- research article
- Published by Springer Nature in International Journal of System Assurance Engineering and Management
- p. 1-9
Abstract
No abstract availableKeywords
This publication has 24 references indexed in Scilit:
- RETRACTED ARTICLE: Design of precoder for a MIMO–NOMA system using Gaussian mixture modellingOptical and Quantum Electronics, 2023
- RETRACTED ARTICLE: Enhancing quality of service in wireless systems using iterative weighted least squares with fuzzy logic integration algorithmOptical and Quantum Electronics, 2023
- Blockchain-enabled bioacoustics signal authentication for cloud-based electronic medical recordsMeasurement: Sensors, 2023
- A provably secure data sharing scheme for smart gas distribution grid using fog computingInternational Journal of Information Technology, 2022
- Cost-Based Heterogeneous Learning Framework for Real-Time Spam Detection in Social Networks With Expert DecisionsIEEE Access, 2021
- Analyzing the effectiveness of semi-supervised learning approaches for opinion spam classificationApplied Soft Computing, 2021
- Detection of Social Network Spam Based on Improved Extreme Learning MachineIEEE Access, 2020
- Detection of spam-posting accounts on TwitterNeurocomputing, 2018
- Detecting Social Spam Campaigns on Twitter2012
- Spam Detection on Twitter Using Traditional Classifiers2011