Biogeography Genetic Algorithm Based Social Platform Spammer Identification Using Content Feature
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International Journal of Engineering Trends and Technology (IJETT) |  |
| © 2020 by IJETT Journal | ||
| Volume-68 Issue-9 |
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| Year of Publication : 2020 | ||
| Authors : Rupali Vishwakarma, Dr. Pratima Gautam |
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| DOI : 10.14445/22315381/IJETT-V68I9P219 |
Citation
MLA Style: Rupali Vishwakarma, Dr. Pratima Gautam "Biogeography Genetic Algorithm Based Social Platform Spammer Identification Using Content Feature" International Journal of Engineering Trends and Technology 68.9(2020):139-145.
APA Style:Rupali Vishwakarma, Dr. Pratima Gautam. Biogeography Genetic Algorithm Based Social Platform Spammer Identification Using Content Feature International Journal of Engineering Trends and Technology, 68(9),139-145.
Abstract
The most popular and leading social network service have the probability of threats and unwanted posts. So to identify and block such Spams, there are a few techniques were developed. Number of researchers have proposed different techniques to identify malicious accounts and spammers over last two decades. This work has also proposed an un-supervised technique for identifying the real users from the social network spammers. Here clustering of social users were done by using tweleve features on the basis of words, hastags, number of blogs (Tweet), URL, etc. Here for unsupervised spammer identification genetic algorithm biogeographic genetic algorithm was used. As this algorithm perform chromosome modification on the basis of immigration and emigration rate, so reaching a good solution is easily achieved. Proposed model cluster the user on the basis of its social activities in articular duration of time. Experiment was done on real dataset from twitter social network. Proposed algorithm BGOA (Biogeographic Optimization Algorithm) for spammer detection in social network was compared with other existing algorithm on different evaluation parameters and results shows that proposed model was better than other.
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Keywords
Online Social Networks (OSNs), Twitter, Spammers, Legitimate users