Biogeography Genetic Algorithm Based Social Platform Spammer Identification Using Content Feature

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2020 by IJETT Journal
Volume-68 Issue-9
Year of Publication : 2020
Authors : Rupali Vishwakarma, Dr. Pratima Gautam
DOI :  10.14445/22315381/IJETT-V68I9P219


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.

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.


[1] Morris, M. and Ogan, C. (1996). “The internet as mass medium”. Journal of communication, 46(1):39-50.
[2] Lee, K., Eo_, B. D., and Caverlee, J. (2011). “Seven Months with the Devils:A Long-Term Study of Content Polluters on Twitter”. In Proc. AAAI Intl. Conf. on Web and Social Media (ICWSM).
[3] Ferrara, E., Varol, O., Davis, C., Menczer, F., and Flammini, A. (2016a). “The rise of social bots”. Communications of the ACM, 59(7):96{104.
[4] Jun, Y., Meng, R., and Johar, G. V. (2017). “Perceived social presence re-duces fact-checking”. Proceedings of the National Academy of Sciences,114(23):5976{5981.
[5] Jagatic, T. N., Johnson, N. A., Jakobsson, M., and Menczer, F. (2007). “Social phishing”. Communications of the ACM, 50(10):94{100.
[6] Mevada D. L., Daxini V., “An opinion spam analyzer for product Reviews using supervised machine Learning method.” pp.03, (2015).
[7] M. N. Istiaq Ahsan , Tamzid Nahian , Abdullah All Kafi , Md. Ismail Hossain , Faisal Muhammad Shah “Review Spam Detection using Active Learning.” 978-1-5090-0996-1, pp.16, (2016).
[8] Michael C., et al. "Survey of review spam detection using machine learning techniques." Journal of Big Data 2.1, pp.9, (2015).
[9] Adike R. G., Reddy V,. “Detection of Fake Review and Brand Spam Using Data Mining Technique.”, pp.02,(2016).
[10] Rajamohana S. P, Umamaheswari K., Dharani M., Vedackshya R., “Survey of review spam detection using machine learning techniques.” ,978-1-50905778-8, pp.17 (2017).
[11] Mubarak, H.; Darwish, K.; Magdy, W. Abusive language detection on Arabic social media. In Proceedings of the First Workshop on Abusive Language Online, Vancouver, BC, Canada, 4–7 August 2017; pp. 52–56.
[12] Ameen, A.K.; Kaya, B. Detecting spammers in twitter network. Int. J. Appl. Math. Electron. Comput. 2017, 5, 71– 75.
[13] Alshehri, A.; Nagoudi, A.; Hassan, A.; Abdul-Mageed, M. “Think before your click: Data and models for adult content in arabic twitter”. In Proceedings of the 2nd Text Analytics for Cybersecurity and Online Safety (TA-COS-2018), 2018.
[14] Boshmaf, Y., Muslukhov, I., Beznosov, K., and Ripeanu, M. (2012). “Key chal- lenges in defending against malicious socialbots”. In Proc. 5th USENIX Conference on Large-Scale Exploits and Emergent Threats (LEET).
[15] Dandan Jiang1, Xiangfeng Luo1, Junyu Xuan, And Zheng Xu .“Sentiment Computing for the News Event Based. on the Social Media Big Data”. Digital Object Identifier 10.1109/ACCESS.2016.2607218 IEEE Acess 2017.
[16] MacArthur R., Wilson E. “The Theory of Biogeography”. Princeton, NJ, USA: Princeton University Press; 1967.
[17] Simon D. Biogeography-based optimization. IEEE Transactions on Evolutionary Computation. 2008;12(6):702– 713. doi: 10.1109/tevc.2008.919004.
[18] Rupali Vishwakarma, Dr. Pratima Gautam . “Un-Supervised Random Forest Tree and Content Feature-Based Blog Spammer Identification”. International Journal of Computer Sciences and Engineering (ISSN: 2347-2693), Vol.7, Issue.9, September 2019.
[19] Muhammad U. S. Khan, Member, Mazhar Ali, Member, Assad Abbas, Student Member, Samee U. Khan, Senior Member and Albert Y. Zomaya. “Segregating Spammers and Unsolicited Bloggers from Genuine Experts on Twitter”. IEEE Computer Society 2016.
[20] Mohammed Alweshah. "Construction biogeography-based optimization algorithm for solving classification problems". Neural Computing and Applications, Springer volume 28 February 2018
[21] Raju Pal, Mukesh Saraswat. "Enhanced Bag of Features Using AlexNet and Improved Biogeography-Based Optimization for Histopathological Image Analysis". 2018 Eleventh International Conference on Contemporary Computing (IC3)

Online Social Networks (OSNs), Twitter, Spammers, Legitimate users