E-Reputation Prediction Model in Online Social Networks

Full Text (PDF, 641KB), PP.17-25

Views: 0 Downloads: 0

Author(s)

Mouna El Marrakchi 1,* Hicham Bensaid 1,2 Mostafa Bellafkih 1

1. National Institute of Posts and Telecommunications, STRS Lab, Rabat, Morocco

2. Mohammed V University in Rabat, Faculty of Sciences, Laboratory of Mathematics, Computing and Applications (LabMiA), BP1014, Rabat, Morocco

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2017.11.03

Received: 4 Apr. 2017 / Revised: 1 Jun. 2017 / Accepted: 7 Jul. 2017 / Published: 8 Nov. 2017

Index Terms

E-reputation, reputation score, reputation prediction, reputation systems, trust systems, online Social Networks

Abstract

E-reputation management has become an important challenge for firms that try to improve their notoriety across the web and more specifically in social media. Indeed, the power of online communities to impact a brand’s image is undeniable and companies need a powerful system to measure their reputation as perceived by connected society. Moreover, they need to follow its variation and forecast its evolution to anticipate any impacting change. For this purpose we have implemented an Intelligent Reputation Measuring System (IRMS) that assesses reputation in online social networks on the basis of members’ activity and popularity. In this paper, we add a predictive module to IRMS that forecasts the evolution of reputation score using influence propagation algorithms.

Cite This Paper

Mouna El Marrakchi, Hicham Bensaid, Mostafa Bellafkih, "E-Reputation Prediction Model in Online Social Networks", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.11, pp.17-25, 2017. DOI:10.5815/ijisa.2017.11.03

Reference

[1]Dijkmans C, Kerkhof P, Beukeboom C J. “A Stage to Engage: Social Media Use and Corporate Reputation“. Tourism Management, 2015, 47, 58-67.
[2]Agarwal J, Osiyevskyy O, Feldman P M. “Corporate Reputation Measurement: Alternative Factor Structures, Nomological Validity, and Organizational Outcomes“. Journal of Business Ethics, 2015, 130(2), 485-506.
[3]Hendrikx, F., Bubendorfer, K., Chard, R. (2015). “Reputation Systems: A Survey and Taxonomy”. Journal of Parallel and Distributed Computing, 75, 184-197.
[4]El Marrakchi M, Bellafkih M, Bensaid H. “Towards Reputation Measurement in Online Social Networks“. In Intelligent Systems and Computer Vision (ISCV), 2015 (pp. 1-8). IEEE.
[5]Sherchan W, Nepal S, Paris C. “A Survey of Trust in Social Networks“. ACM Computing Surveys (CSUR), 2013, 45(4), 47.
[6]Hamdi S. “Computational Models of Trust and Reputation in Online Social Networks” (Doctoral dissertation, Université Paris-Saclay), 2016.
[7]Gal-Oz N, Gudes E, Hendler D. “A Robust and Knot-Aware Trust-Based Reputation Model“. In Trust Management II , 2008, pp. 167-182. Springer US.
[8]Alchiekh Haydar C. “Les Systèmes de Recommandation à Base de Confiance“ (Doctoral dissertation, Université de Lorraine), 2014.
[9]Jøsang A. “Bayesian Reputation Systems”. In Subjective Logic, 2016, pp. 289-302. Springer International Publishing.
[10]Zong B, Xu F, Jiao J, Lv J. “A Broker-Assisting Trust and Reputation System Based on Artificial Neural Network“. In Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on (pp. 4710-4715). IEEE.
[11]Nepal S, Paris C, Bista S K, Sherchan W. “A Trust Model–Based Analysis of Social Networks”, International Journal of Trust Management in Computing and Communications, 2013, pp, 3-22.
[12]Ziegler C N, Lausen G. “Propagation Models for Trust and Distrust in Social Networks“. Information Systems Frontiers, 2005, 7(4-5), 337-358.
[13]Mohan, A., & Remya, G. (2017). “A Review on Large Scale Graph Processing Using Big Data Based Parallel Programming Models”. International Journal of Intelligent Systems and Applications, 9(2), 49.
[14]Liu, S., Yu, H., Miao, C., & Kot, A. C. (2013, May). “A Fuzzy Logic Based Reputation Model Against Unfair Ratings”. In Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems (pp. 821-828). International Foundation for Autonomous Agents and Multiagent Systems.
[15]Vavilis, S., Petković, M., & Zannone, N. (2014). “A Reference Model for Reputation Systems”. Decision Support Systems, 61, 147-154.
[16]Narendra B, Sai K U, Rajesh G, Hemanth K, Teja M C, Kumar K D. “Sentiment Analysis on Movie Reviews: A Comparative Study of Machine Learning Algorithms and Open Source Technologies”. International Journal of Intelligent Systems and Applications (IJISA), 2016, 8(8), 66.
[17]El Marrakchi M, Bensaid H, Bellafkih M. “Intelligent Reputation Scoring in Social Networks: Use Case of Brands of Smartphones“. In Intelligent Systems: Theories and Applications (SITA), 2016 11th International Conference on (pp. 1-6). IEEE.
[18]Chen, W., Lakshmanan, L. V., & Castillo, C. (2013). “Information and Influence Propagation in Social Networks“. Synthesis Lectures on Data Management, 5(4), 1-177.
[19]Richardson M, Domingos P. “Mining Knowledge-Sharing Sites for Viral Marketing,” in Proc. of the Eighth ACM SIGKDD Int. Conf. On Knowledge Discovery and Data Mining (KDD’02), 2002.
[20]Kempe D, Kleinberg J M, Tardos E. “Maximizing the Spread of Influence Through a Social Network“, in Proc. of the Ninth ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD’03), 2003.
[21]Chen W et al. (2011, April). “Influence Maximization in Social Networks When Negative Opinions May Emerge and Propagate“. In SDM (Vol. 11, pp. 379-390).
[22]Lagnier C, Denoyer L, Gaussier E, Gallinari P. (2013, March). “Predicting Information Diffusion in Social Networks Using Content and User’s Profiles”. In European conference on information retrieval (pp. 74-85). Springer Berlin Heidelberg.
[23]Kutzkov K, Bifet A, Bonchi F, Gionis A. “Strip: Stream Learning of Influence Probabilities“. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, 2013, August, (pp. 275-283). ACM.
[24]Goyal A, Bonchi F, Lakshmanan L. V. “Learning Influence Probabilities in Social Networks“. In Proceedings of the third ACM international conference on Web search and data mining, 2010, February, (pp. 241-250). ACM.
[25]Saito K, Nakano R, Kimura M. “Prediction of Information Diffusion Probabilities for Independent Cascade Model”. In International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 2008, (pp. 67-75). Springer Berlin Heidelberg.
[26]Tang J, Sun J, Wang C, Yang Z. “Social Influence Analysis in Large-Scale Networks“. In Proc. of the 15th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD’09).