Abstract
Social network mining is a growing research area which combines together different fields such as machine learning, graph theory, parallel algorithms, data mining, optimization, etc., with the aim of dealing with issues like behavior analysis, finding interacting groups, finding influencers, information diffusion, etc. in a social network. This paper deals with one of these important issues i.e., Influencer Identification in social networks. This paper presents a data mining modelling approach for a twitter network, to find the most influential user among the given pair of users. This could be scaled over the entire network. We used a data mining model to score the test data and predict the influential user among the given pair of users. This approach of modeling can potentially be used for building many of the marketing and sales strategies wherein the influencer may be motivated for diffusing information or new ideas.
This is a preview of subscription content, log in via an institution to check access.
References
More, J.S., Lingam, C.: Reality mining based on social network analysis. In: Proceedings of IEEE International Conference on Communication Information and Computing Technology (ICCIT), pp. 1–6 (2015)
Huang, F., Cheng, N.X., Xiao, R.: An approach to mining social networks in chat room. J. Comput. Inf. Syst. 1, 135–143 (2011)
Kelman, H.: Compliance, identification, and internalization: three processes of attitude change. J. Conflict Resolut. 2, 51–60 (1958)
Chen, Duanbing, Lü, L., Shang, M.S., Zhang, Y.C., Zhou, T.: Identifying influential nodes in complex networks. Phys. A Stat. Mechan. Appl. 391(4), 1777–1787 (2011)
Kiss, Christine, Bichler, Martin: Identification of influencers- measuring influence in customer networks. Decis. Support Syst. 46, 233–253 (2008)
Li, N., Gillet, D.: Identifying influential scholars in academic social media platforms. In: ASONAM Proceedings IEEE/ACM International Conference on Advances in Social Network Analysis and Mining, pp. 608–614 (2013)
Katona, Z., Zubcsek, P.P., Sarvary, M.: Network effects and personal influences: the diffusion of an online social network. J. Mark. Res. 48(3), 425–443 (2011)
Chatterjee, P.: Drivers of new product recommending and referrel behavior at social network sites. Int. J. Advertising 30(1), 77–101 (2011)
Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J: Everyone’s an influencer: quantifying influence on twitter. In: WSDM Proceedings of Fourth ACM International Conference on Web Search and Data Mining, pp. 65–74 (2011)
Anger, I., Kittl, C.: Measuring influence on twitter. In: International Conference on Knowledge management and Knowledge Technologies. ACM (2011)
Bakshy, E., Karrer, B., Adamic, L.A.: Social influence and the diffusion of user-created content. In: Proceedings of the 10th ACM conference on Electronic commerce, pp. 325–334 (2009)
James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning, pp. 130–137. Springer, New York (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
More, J.S., Lingam, C. (2016). A Scalable Data Mining Model for Social Media Influencer Identification. In: Unal, A., Nayak, M., Mishra, D.K., Singh, D., Joshi, A. (eds) Smart Trends in Information Technology and Computer Communications. SmartCom 2016. Communications in Computer and Information Science, vol 628. Springer, Singapore. https://doi.org/10.1007/978-981-10-3433-6_75
Download citation
DOI: https://doi.org/10.1007/978-981-10-3433-6_75
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3432-9
Online ISBN: 978-981-10-3433-6
eBook Packages: Computer ScienceComputer Science (R0)