Abstract
Social media is playing an important role in our daily life. People usually hold various identities on different social media sites. User-contributed Web data contains diverse information which reflects individual interests, political opinions and other behaviors. To integrate these behaviors information, it is of value to identify users across social media sites. This paper focuses on the challenge of identifying unknown users across different social media sites. A method to relate user’s identities across social media sites by mining users’ behavior information and features is introduced. The method has two key components. The first component distinguishes different users by analyzing their common social network behaviors and finding strong opposing characters. The second component constructs a model of behavior features that helps to obtain the difference of users across social media sites. The method is evaluated through two experiments on Twitter and Sina Weibo. The results of experiments show that the method is effective.
Sponsored by National Key Technology Research and Development Program No.2012BAH38B04, National Key fundamental Research and Development Program No.2013CB329601.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zafarani, R., Liu, H.: Connecting users across social media sites: a behavioral-modeling approach. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2013)
Ahmed, A., Xing, E.P.: Staying informed: supervised and semi-supervised multi-view topical analysis of ideological perspective. In: EMNLP, pp. 1140–1150 (2010)
Amitay, E., Yogev, S., Yom-Tov, E.: Serial Sharers: Detecting Split Identities of Web Authors. In: SIGIR PAN Workshop (2007)
Cilibrasi, R., Vitanyi, P.M.B.: Clustering by Compression. IEEE Transactions on Information Theory 51(4), 1523–1545 (2005)
Albakour, M.-D., Macdonald, C., Ounis, I.: Identifying Local Events by Using Microblogs as Social Sensors. In: OAIR 2013, May 22-24 (2013)
Zheng, R., Li, J., Chen, H., Huang, Z.: A Framework for Authorship Identification of Online Messages: Writing-style Features and Classification Techniques. JASIST 57(3), 378–393 (2006)
De Vel, O., Anderson, A., Corney, M., Mohay, G.: Mining E-mail Content for Author Identification forensics. ACM Sigmod Record 30(4), 55–64 (2001)
Albakour, M.D., Macdonald, C., Ounis, I., Pnevmatikakis, A., Soldatos, J.: SMART: An open source framework for searching the physical world. In: OSIR at SIGIR 2012 (2012)
Bache, K., Newman, D., Smyth, P.: Text-based measures of document diversity. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2013)
Tang, J., Zhang, M., Mei, Q.: One theme in all views: modeling consensus topics in multiple contexts. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2013)
Becker, H., et al.: Identifying content for planned events across social media sites. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining. ACM (2012)
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: Real-time event detection by social sensors. In: Proceedings of the 19th International World Wide Web Conference, WWW 2010 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Nie, Y., Huang, J., Li, A., Zhou, B. (2014). Identifying Users Based on Behavioral-Modeling across Social Media Sites. In: Chen, L., Jia, Y., Sellis, T., Liu, G. (eds) Web Technologies and Applications. APWeb 2014. Lecture Notes in Computer Science, vol 8709. Springer, Cham. https://doi.org/10.1007/978-3-319-11116-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-11116-2_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11115-5
Online ISBN: 978-3-319-11116-2
eBook Packages: Computer ScienceComputer Science (R0)