Geodesic
Twitter users popularity estimation using expert finding
Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika, Tome 3 (2014) no. 2, pp. 63-76 Cet article a éte moissonné depuis la source Math-Net.Ru

Voir la notice de l'article

In this paper we have considered mixed language model that is used for experts finding in areas such as social network analysis and information retrieval, and proposed an adaptation of this model for the social network Twitter. We also have reviewed Twitter popularity metrics and proposed Twitter users' popularity estimation approach based on expert finding, which allows to rank users according to the probability of user being an expert in given query, and have implemented a prototype for data collection and popularity estimation, based on our approach.
Keywords: social network analysis, information retrieval, data mining, expert finding, popularity analysis. 
@article{VYURV_2014_3_2_a4,
     author = {R. M. Miniakhmetov and E. O. Tsatsina},
     title = {Twitter users popularity estimation using expert finding},
     journal = {Vestnik \^U\v{z}no-Uralʹskogo gosudarstvennogo universiteta. Seri\^a Vy\v{c}islitelʹna\^a matematika i informatika},
     pages = {63--76},
     year = {2014},
     volume = {3},
     number = {2},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/VYURV_2014_3_2_a4/}
}
TY  - JOUR
AU  - R. M. Miniakhmetov
AU  - E. O. Tsatsina
TI  - Twitter users popularity estimation using expert finding
JO  - Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika
PY  - 2014
SP  - 63
EP  - 76
VL  - 3
IS  - 2
UR  - http://geodesic.mathdoc.fr/item/VYURV_2014_3_2_a4/
LA  - ru
ID  - VYURV_2014_3_2_a4
ER  - 
%0 Journal Article
%A R. M. Miniakhmetov
%A E. O. Tsatsina
%T Twitter users popularity estimation using expert finding
%J Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika
%D 2014
%P 63-76
%V 3
%N 2
%U http://geodesic.mathdoc.fr/item/VYURV_2014_3_2_a4/
%G ru
%F VYURV_2014_3_2_a4
R. M. Miniakhmetov; E. O. Tsatsina. Twitter users popularity estimation using expert finding. Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika, Tome 3 (2014) no. 2, pp. 63-76. http://geodesic.mathdoc.fr/item/VYURV_2014_3_2_a4/

[1] J. Zhang, J. Tang, J. Li, “Expert Finding in A Social Network”, Advances in Databases: Concepts, Systems and Applications, Lecture Notes in Computer Science, 4443, Springer, Heidelberg, 2007, 1066–1069 | DOI

[2] A. Bozzon, M. Brambilla, S. Ceri, M. Silvestri, G. Vesci, “Choosing the Right Crowd: Expert Finding in Social Networks”, Proceedings of the 16th International Conference on Extending Database Technology (EDBT '13), 2013, 637–648 | DOI

[3] J. Zhang, J. Tang, L. Liu, J. Li, “A Mixture Model for Expert Finding”, Advances in Knowledge Discovery and Data Mining, 12th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD '08), Lecture Notes in Computer Science, 5012, Springer, Heidelberg, 2008, 466–478 | DOI

[4] C. Manning, P. Raghavan, H. Schütze, Introduction to Information Retrieval, Cambridge University Press, 2008, 496 pp.

[5] J. Ponte, W. Croft, “A Language Modeling Approach to Information Retrieval”, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '98), 1998, 275–281 | DOI

[6] D. Heimstra, “A Linguistically Motivated Probabilistic Model of Information Retrieval”, Proceedings of the Second European Conference on Research and Advanced Technology for Digital Libraries, 1998, 569–584

[7] H. Steck, “Item Popularity and Recommendation Accuracy”, Proceedings of the Second European Conference on Research and Advanced Technology for Digital Libraries (RecSys '11), 2011, 125–132 | DOI

[8] Y. Cha, B. Bin, C. Hsieh, J. Cho, “Incorporating Popularity in Topic Models for Social Network Analysis”, Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '13), 2013, 223–232 | DOI

[9] Twitter API Library for Python Documentation, (data obrascheniya: 24.04.2014) http://pythonhosted.org//tweepy/

[10] Twitter REST API Documentation, (accessed: 24.04.2014) https://dev.twitter.com/docs/api/1.1

[11] M. Stonebraker, G. Kemnitz, “The POSTGRES Next-generation Database Management System”, Communications of the ACM, 34:10 (1991), 78–92 | DOI

[12] A. Borodin, R. El-Yaniv, Online Computation and Competitive Analysis, Cambridge University Press, 1998, 432 pp.

[13] D.D. Yantsen, M.L. Zymbler, “A Representative Sampling Method for Parallel DBMS”, Parallel Computational Technologies (PCT'2014): Proceedings of the International Scientific Conference (April 1-3, 2014, Rostov-on-Don), Publishing of the South Ural State University, Chelyabinsk, 2014, 381 pp.

[14] C.S. Pan, “An Approach for Very Large Graph Partitioning by Means of Parallel DBMS”, Bulletin of the South Ural State University. Series “Computational Mathematics and Software Engineering”, 47(306):2 (2012), 127–132 | DOI

[15] Tracking the Most Popular Users of a Certain Microblogging/Social Network Tool «Twitaholic», (data obrascheniya: 24.04.2014) http://twitaholic.com/

[16] Machine Learning Library for Python Documentation “scikitlearn”, (data obrascheniya: 24.04.2014) http://scikit-learn.org/stable/documentation.html

[17] H. Zhu, E. Chen, H. Cao, “Finding Experts in Tag Based Knowledge Sharing Communities”, Proceedings of the 5th International Conference on Knowledge Science, Engineering and Management (KSEM '11), 2011, 183–195

[18] J. Han, J. Pei, Y. Yin, R. Mao, “Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach”, Data Mining and Knowledge Discovery, 8:1 (2004), 53–87 | DOI

[19] D. Blei, A. Ng, Y. Yin, M. Jordan, “Latent Dirichlet Allocation”, The Journal of Machine Learning Research, 3 (2003), 993–1022