A New Supervised Epidemic Model for Intelligent Viral Content Classification

Year 2016, Volume: 4 Issue: Special Issue-1, 216 - 221, 26.12.2016

Abdulkerim Şenoğlu , Uraz Yavanoğlu , Suat Özdemir

Abstract

In this study, we propose an
information diffusion model which is based on neural networks, artificial
intelligence and supervised epidemic approach. We collected epidemically
diffused data from Twitter with supervision to create a ranking system that forms
the base of our diffusion model. The collected data is also used to train the
proposed model. The outputs of the proposed model are shown to be useful for
the provenance problem and the diffusion prediction systems in both physical
and social networks. Knowing the viral content beforehand can be used in
advertisement, industry, politics or any other end user that wants to reach a large
number of people. Our performance analysis show that the proposed model can
achieve over 90% training success rate and 78% test success rate of classifying
viral content which is better than some of the existing models.

Keywords

Artificial Neural Networks (ANN), Epidemic approach, Supervised learning, Information diffusion

References

• [1] Barbier, G., Feng, Z., Gundecha, P., & Liu, H. (2013). Provenance data in social media. Synthesis Lectures on Data Mining and Knowledge Discovery, 4(1), 1-84.
• [2] Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012, April). The role of social networks in information diffusion. In Proceedings of the 21st international conference on World Wide Web (pp. 519-528). ACM.
• [3] Milgram, S. (1967). The small world problem. Psychology today, 2(1), 60-67.
• [4] Wu, F., Huberman, B. A., Adamic, L. A., & Tyler, J. R. (2004). Information flow in social groups. Physica A: Statistical Mechanics and its Applications, 337(1), 327-335.
• [5] Zafarani, R., Abbasi, M. A., & Liu, H. (2014). Social media mining: an introduction. Cambridge University Press.
• [6] Keeling, M. J., & Eames, K. T. (2005). Networks and epidemic models. Journal of the Royal Society Interface, 2(4), 295-307.
• [7] Jin, Fang, et al. "Epidemiological modeling of news and rumors on Twitter. "Proceedings of the 7th Workshop on Social Network Mining and Analysis. ACM, 2013
• [8] Yang, Jaewon, and Jure Leskovec. "Modeling information diffusion in implicit networks." Data Mining (ICDM), 2010 IEEE 10th International Conference on. IEEE, 2010.
• [9] Leskovec, Jure, Lada A. Adamic, and Bernardo A. Huberman. "The dynamics of viral marketing." ACM Transactions on the Web (TWEB)1.1 (2007): 5
• [10] Bakshy, Eytan, et al. "The role of social networks in information diffusion. “Proceedings of the 21st international conference on World Wide Web. ACM, 2012
• [11] Aral, Sinan, and Dylan Walker. "Identifying influential and susceptible members of social networks." Science 337.6092 (2012): 337-341.
• [12] Guille, Adrien, and Hakim Hacid. "A predictive model for the temporal dynamics of information diffusion in online social networks." Proceedings of the 21st international conference companion on World Wide Web. ACM, 2012.
• [13] Md. S. Bayzid, A. Iqbal, C. S. Hyder, M. T. Irfan,” Application of Artificial Neural Network in Social Computing in the Context of Third World Countries”, 5th International Conference on Electrical and Computer Engineering, ICECE, 2008
• [14] M. Thelwall, D. Wilkinso, S. Uppal, “Data Mining Emotion in Social Network Communication: Gender Differences in MySpace”, Journal of the American Society for Information Science and Technology, 2010.
• [15] Zhao, J., Lui, J. C., Towsley, D., Guan, X., & Zhou, Y. (2011, April). Empirical analysis of the evolution of follower network: A case study on Douban. InComputer Communications Workshops (INFOCOM WKSHPS), 2011 IEEE Conference on (pp. 924-929). IEEE.
• [16] Ghenname, M., Abik, M., Ajhoun, R., Subercaze, J., Gravier, C., & Laforest, F. (2013, November). Personalized recommendation based hashtags on e-learning systems. In ISKO-Maghreb, 2013 3rd International Symposium (pp. 1-8). IEEE.
• [17] Li, L. H., Lee, F. M., & Liu, W. J. (2006). The timely product recommendation based on RFM method.
• [18] Shani, G., Meisles, A., Gleyzer, Y., Rokach, L., & Ben-Shimon, D. (2007). A stereotypes-based hybrid recommender system for media items. In Workshop on Intelligent Techniques for Web Personalization, Vancouver, Canada (pp. 76-83).
• [19] Diao, Q., Qiu, M., Wu, C. Y., Smola, A. J., Jiang, J., & Wang, C. (2014, August). Jointly modeling aspects, ratings and sentiments for movie recommendation (jmars). In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 193-202). ACM.
• [20] Kong, D., & Zhai, Y. (2012, November). Trust based recommendation system in service-oriented cloud computing. In Cloud and Service Computing (CSC), 2012 International Conference on (pp. 176-179). IEEE.
• [21] E. Tyugu, “Artificial Intelligence in Cyber Defense”, 3rd International Conference on Cyber Conflict, pp. 1-11, Tallinn, 2011
• [22] W. Britt, S. Gopalaswamy, J. A. Hamilton, G. V. Dozier, K. H. Chang, “Computer Defense Using Artificial Intelligence”, Spring simulation multiconference, Vol. 3, pp. 378-386, 2007
• [23] L.A. M. Pereira, L. C. S. Afonso, J. P. Papa, Z. A. Vale, C. C. O. Ramos, D.S. Gastaldello, A.N. Souza , “Multilayer Perceptron Neural Networks Training Through Charged System Search and its Application for Non-Technical Losses Detection”, Innovative Smart Grid Technologies Latin America (ISGT LA), pp. 1-6, Sao Paulo, 2013
• [24] Y. Lin, “Application of Extracted Rules from a Multilayer Perceptron Network to Moulding Machine Cycle Time Improvement”, IEEE Transactions On Components, Packaging And Manufacturing Technology, Vol. 1(3), pp. 436 – 445, 2011
• [25] S. Sagiroglu, U. Yavanoglu, E. N. Guven, “Web based Machine Learning for Language Identification and Translation”, International Conference on Machine Learning and Applications, pp. 280-285, Cincinnati, OH, 2007
• [26] U. Yavanoglu, O. Kaplan, H. Atli, G. Tanis, O. Milletsever, S. Sagiroglu, “Intelligent Decision Support System For Energy Investments”, 12th International Conference on Machine Learning and Applications (ICMLA), vol. 2, pp. 224-231, Miami, FL, 2013
• [27] Van Mieghem, P., Omic, J., & Kooij, R. (2009). Virus spread in networks.IEEE/ACM Transactions on Networking, 17(1), 1-14.