Abstract
The popularity of social media as an information source, in the recent years has spawned several interesting applications, and consequently challenges to using it effectively. Identifying and targeting influential individuals on sites is a crucial way to maximize the returns of advertising and marketing efforts. Recently, this problem has been well studied in the context of blogs, microblogs, and other forms of social media sites. Understanding how these users behave on a social media site and even across social media sites will lead to more effective strategies. In this book chapter, we present existing techniques to identify influential individuals in a social media site. We present a user identification strategy, which can help us to identify influential individuals across sites. Using a combination of these approaches we present a study of the characteristics and behavior of influential individuals across sites. We evaluate our approaches on several of the popular social media sites. Among other interesting findings, we discover that influential individuals on one site are more likely to be influential on other sites as well. We also find that influential users are more likely to connect to other influential individuals.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
Interested readers can find more details in [2].
- 10.
This property is most difficult to approximate using some statistics. Eloquence of an article could be gauged using more sophisticated linguistic based measures.
- 11.
- 12.
- 13.
References
Adar, E., Adamic, L.A.: Tracking information epidemics in blogspace. In: WI’05: Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI’05), Washington, DC, USA, pp. 207–214. IEEE Comput. Soc., Los Alamitos (2005). doi:10.1109/WI.2005.151
Agarwal, N., Liu, H., Tang, L., Yu, P.S.: Identifying the influential bloggers in a community. In: Proceedings of the International Conference on Web Search and Web Data Mining, pp. 207–218. ACM, New York (2008)
Amitay, E., Yogev, S., Yom-Tov, E.: Serial sharers: Detecting split identities of web authors. In: Workshop on Plagiarism Analysis, Authorship Identification, and Near-Duplicate Detection, Amsterdam, Netherlands, July (2007). http://einat.webir.org/SIGIR_PAN_workshop_2007.pdf
Anderson, C.: The Long Tail: Why the Future of Business Is Selling Less of More. Hyperion Books, New York (2008)
Cilibrasi, R., Vitanyi, P.M.B.: Clustering by compression. IEEE Trans. Inf. Theory 51(4), 1523–1545 (2005)
Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the internet topology. In: Proceedings of the Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, pp. 251–262. ACM, New York (1999)
Gill, K.E.: How can we measure the influence of the blogosphere. In: WWW 2004 Workshop on the Weblogging Ecosystem: Aggregation, Analysis and Dynamics, New York. Citeseer (2004)
Golbeck, J., Hendler, J.: Filmtrust: Movie recommendations using trust in web-based social networks. In: Proceedings of the IEEE Consumer Communications and Networking Conference, vol. 96. Citeseer (2006)
Goldenberg, J., Libai, B., Muller, E.: Talk of the network: A complex systems look at the underlying process of word-of-mouth. Mark. Lett. 12(3), 211–223 (2001)
Gruhl, D., Guha, R., Liben-Nowell, D., Tomkins, A.s.: Information diffusion through blogspace. In: Proceedings of the 13th International Conference on World Wide Web, pp. 491–501. ACM, New York (2004)
Katz, E.: The two-step flow of communication: An up-to-date report on an hypothesis. Public Opin. Q. 21(1), 61–78 (1957)
Keller, E., Berry, J.: One American in Ten Tells the Other Nine How to Vote, Where to Eat and, What to Buy. They Are the Influentials. The Free Press, New York (2003)
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM, New York (2003)
Kolari, P., Finin, T., Joshi, A.: SVMs for the blogosphere: Blog identification and splog detection. In: AAAI Spring Symposium on Computational Approaches to Analyzing Weblogs (2006)
Kumar, R., et al.: On the bursty evolution of blogspace. In: 12th International Conference on World Wide Web (2003)
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)
Lin, Y.-R., Sundaram, H., Chi, Y., Tatemura, J., Tseng, B.L.: Detecting splogs via temporal dynamics using self-similarity analysis. ACM Trans. Web 2(1), 1–35 (2008)
Merton, R.K.: Social Theory and Social Structure. Free Press, New York (1968)
Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, p. 42. ACM, New York (2007)
Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing (2002)
Rogers, E.M., Shoemaker, F.F.: Communication of innovations; a cross-cultural approach (1971)
Zafarani, R., Liu, H.: Connecting corresponding identities across communities. In: Proceedings of the 3rd International Conference on Weblogs and Social Media (ICWSM09) (2009)
Acknowledgements
This research was funded in part by the National Science Foundation’s Social-Computational Systems (SoCS) program and Human Centered Computing (HCC) program within the Directorate for Computer and Information Science and Engineering’s Division of Information and Intelligent Systems (award numbers: IIS - 1110868 and IIS - 1110649) and by the U.S. Office of Naval Research (award number: N000141010091). We gratefully acknowledge this support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag London
About this chapter
Cite this chapter
Agarwal, N., Kumar, S., Gao, H., Zafarani, R., Liu, H. (2012). Analyzing Behavior of the Influentials Across Social Media. In: Cao, L., Yu, P. (eds) Behavior Computing. Springer, London. https://doi.org/10.1007/978-1-4471-2969-1_1
Download citation
DOI: https://doi.org/10.1007/978-1-4471-2969-1_1
Publisher Name: Springer, London
Print ISBN: 978-1-4471-2968-4
Online ISBN: 978-1-4471-2969-1
eBook Packages: Computer ScienceComputer Science (R0)