Abstract
Twitter has been the focus of analysis in recent years due to various interesting and challenging problems, one of them being Clustering of its Users based on their interests. For graphs, there are many clustering approaches which look at either the structure or at its contents. However, when we consider real world data such as Twitter Data, structural approaches may produce many different user clusters with similar interests. Similarly, content-based clustering approaches on Twitter Data produce inferior results due limited length of Tweet and due to lots of garbled data. Hence, these approaches cannot be directly used for practical applications. In this paper, we have made an effort to cluster Twitter Users based on their interest, looking at both the structure of the graph generated using Twitter Data, as well as its contents. By combining these approaches, we improve our results compared to the existing techniques, thereby generating results befitting the practical applications.
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Notes
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Arashi: A Japanese idol group.
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AKB (AKB48): A Japanese idol girls group.
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Hanyu (Yuzuru Hanyu): Japanese figure skater & 2014 Olympic champion.
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Yamashita (Tomohisa Yamashita): Japanese actor, singer, and TV host.
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References
Xu, X., Yuruk, N., Feng, Z., Schweiger, T.A.: Scan: a structural clustering algorithm for networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 824–833. ACM (2007)
Ding, C.H., He, X., Zha, H., Gu, M., Simon, H.D.: A min-max cut algorithm for graph partitioning and data clustering. In: 2001 Proceedings of the IEEE International Conference on Data Mining, ICDM 2001, pp. 107–114. IEEE (2001)
Newman, M.E., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)
Leskovec, J., Lang, K.J., Dasgupta, A., Mahoney, M.W.: Statistical properties of community structure in large social and information networks. In: Proceedings of the 17th International Conference on World Wide Web, pp. 695–704. ACM (2008)
Zhang, Y., Wu, Y., Yang, Q.: Community discovery in Twitter based on user interests. J. Comput. Inf. Syst. 8(3), 991–1000 (2012)
Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. (TOIS) 20(4), 422–446 (2002)
Haveliwala, T.: Topic-sensitive pagerank. In: Proceedings of the 11th International Conference on World Wide Web, Honolulu, Hawaii, USA, pp. 517–526 (2002)
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, P.K.: Measuring user influence in Twitter: the million follower fallacy. In: ICWSM, vol. 10, pp. 10–17 (2010)
Avnit, A.: The million followers fallacy. http://blog.pravdam.com/the-million-followers-fallacy-guest-post-by-adi-avnit/ (2009). Accessed 2 Aug 2016
Hayashi, K., Maehara, T., Toyoda, M., Kawarabayashi, K.-I.: Real-time top-r topic detection on Twitter with topic hijack filtering. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2015, pp. 417–426 (2015)
Weng, J., Lim, E.-P., Jiang, J., He, Q.: Twitterrank: finding topic-sensitive influential twitterers. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, WSDM 2010, pp. 261–270 (2010)
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Naik, A., Maeda, H., Kanojia, V., Fujita, S. (2017). Scalable Twitter User Clustering Approach Boosted by Personalized PageRank. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10234. Springer, Cham. https://doi.org/10.1007/978-3-319-57454-7_37
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DOI: https://doi.org/10.1007/978-3-319-57454-7_37
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