Detecting Streaming of Twitter Spam Using Hybrid Method

Twitter, the social network which evolving faster and regular usage by millions of people and who become addicted to it. So spam playing a major role for Twitter users to distract them and grab their attention over them. Spammers actually detailed like who send unwanted and irrelevant messages or we...

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Bibliographic Details
Published in:Wireless personal communications 2018-11, Vol.103 (2), p.1353-1374
Main Authors: Senthil Murugan, N., Usha Devi, G.
Format: Article
Language:English
Subjects:
Algorithms
Communications Engineering
Computer Communication Networks
Decision trees
Engineering
Feature extraction
Genetic algorithms
Machine learning
Networks
Particle swarm optimization
Real time
Researchers
Signal,Image and Speech Processing
Social networks
Websites
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Summary:Twitter, the social network which evolving faster and regular usage by millions of people and who become addicted to it. So spam playing a major role for Twitter users to distract them and grab their attention over them. Spammers actually detailed like who send unwanted and irrelevant messages or websites and promote them to several users. To overcome the problem many researchers proposed some ideas using some machine learning algorithms to detect the spammers. In this research work, a new hybrid approach is proposed to detect the streaming of Twitter spam in a real-time using the combination of a Decision tree, Particle Swarm Optimization and Genetic algorithm. Twitter has given access to the researchers to get tweets from its Twitter-API for real-time streaming of tweet data which they can get direct access to public tweets. Here 600 million tweets are created by using URL based security tool and further some features are extracted for representation of tweets in real-time detection of spam. In addition, our research results are compared with other hybrid algorithms which a better detection rate is given by our proposed work.
ISSN:0929-6212
1572-834X
DOI:10.1007/s11277-018-5513-z