Abstract
We propose a method for computing user similarity based on a network representing the semantic relationships between the words occurring in the same tweet and the related topics. We use such specially crafted network to define several user profiles to be compared with cosine similarity. We also describe an initial experimental activity to study the effectiveness on a limited dataset.
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
Abdel-Hafez, A., Xu, Y.: A survey of user modelling in social media websites. Computer and Information Science 6(4), 59 (2013)
Anderson, A., Huttenlocher, D., Kleinberg, J., Leskovec, J.: Effects of user similarity in social media. In: WSDM, pp. 703–712. ACM (2012)
Campbell, W.M., Dagli, C.K., Weinstein, C.J.: Social Network Analysis with Content and Graphs. Lincoln Laboratory Journal 20(1) (2013)
Kwok, K.L.: A neural network for probabilistic information retrieval. In: SIGIR 1989, pp. 21–30 (1989)
Lane, N.D., et al.: Exploiting social networks for large-scale human behavior modeling. IEEE Pervasive Computing 10(4), 45–53 (2011)
Li, X., Wang, M., Liang, T.-P.: A multi-theoretical kernel-based approach to social network-based recommendation. Decision Support Systems (2014)
Liu, H., Hu, Z., Mian, A., Tian, H., Zhu, X.: A new user similarity model to improve the accuracy of collaborative filtering. Knowledge-Based Systems 56, 156–166 (2014)
Medo, M.: Network-based information filtering algorithms: Ranking and recommendation. In: Dynamics on and of Complex Networks, vol. 2, pp. 315–334. Springer, New York (2013)
Mizzaro, S., Pavan, M., Scagnetto, I., Valenti, M.: Short text categorization exploiting contextual enrichment and external knowledge. In: Proc. of SoMeRA 2014, SIGIR 2014 (2014)
Orlandi, F., Breslin, J., Passant, A.: Aggregated, interoperable and multi-domain user profiles for the social web. In: 8th Int. Conf. on Semantic Systems, pp. 41–48. ACM (2012)
Tao, K., Abel, F., Gao, Q., Houben, G.-J.: TUMS: Twitter-based user modeling service. In: GarcÃa-Castro, R., Fensel, D., Antoniou, G. (eds.) ESWC 2011. LNCS, vol. 7117, pp. 269–283. Springer, Heidelberg (2012)
Wilkinson, R., Hingston, P.: Using the cosine measure in a neural network for document retrieval. In: ACM SIGIR Conf. on Research and Development in IR, pp. 202–210. ACM (1991)
Yan, Q., Wu, L., Zheng, L.: Social network based microblog user behavior analysis. Physica A: Statistical Mechanics and its Applications 392(7), 1712–1723 (2013)
Zhang, Y., Wu, Y., Yang, Q.: Community discovery in twitter based on user interests. Journal of Computational Information Systems 8(3), 991–1000 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Mizzaro, S., Pavan, M., Scagnetto, I. (2015). Content-Based Similarity of Twitter Users. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds) Advances in Information Retrieval. ECIR 2015. Lecture Notes in Computer Science, vol 9022. Springer, Cham. https://doi.org/10.1007/978-3-319-16354-3_56
Download citation
DOI: https://doi.org/10.1007/978-3-319-16354-3_56
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16353-6
Online ISBN: 978-3-319-16354-3
eBook Packages: Computer ScienceComputer Science (R0)