Abstract
In this paper, we address the problem of finding authoritative users in a micro-blogging service, Twitter, which is one of the most popular micro-blogging services [1]. Twitter has been gaining a public attention as a new type of information resource, because an enormous number of users transmit diverse information in real time. In particular, authoritative users who frequently submit useful information are considered to play an important role, because useful information is disseminated quickly and widely. To identify authoritative users, it is important to consider actual information flow in Twitter. However, existing approaches only deal with relationships among users. In this paper, we propose TURank (Twitter User Rank), which is an algorithm for evaluating users’ authority scores in Twitter based on link analysis. In TURank, users and tweets are represented in a user-tweet graph which models information flow, and ObjectRank is applied to evaluate users’ authority scores. Experimental results show that the proposed algorithm outperforms existing algorithms.
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References
Twitter, http://twitter.com
Twitter API, http://apiwiki.twitter.com/Twitter-API-Documentation
Balmin, A., Hristidis, V., Papakonstantinou, Y.: Objectrank: Authority-based keyword search in databases. In: VLDB (2004)
Boyd, D., Golder, S., Lotan, G.: Tweet, tweet, retweet: Conversational aspects of retweeting on twitter. In: HICSS-43, January 6. IEEE, Kauai (2010)
Honeycutt, C., Herring, S.C.: Beyond microblogging: Conversation and collaboration in twitter. In: Proc. 42nd HICSS. IEEE Press, Los Alamitos (2009)
Huberman, B.A., Romero, D.M., Wu, F.: Social networks that matter: Twitter under the microscope. First Monday 14(1) (January 5, 2009)
Java, A., Song, X., Finn, T., Tseng, B.: Why we twitter: Understanding microblogging usage and communities. In: Joint 9th WEBKDD and 1st SNA-KDD Workshop, San Jose, CA (2007)
Kleinberg, J.: Authoritative Sources in a Hyperlinked Environment. In: Proc. of the 9th ACM SIAM Symposium on Discrete Algorithms (SODA 1998), pp. 668–677 (1998)
Leavitt, A., Burchard, E., Fisher, D., Gilbert, S.: The influentials: New approaches for analyzing influence on twitter. A Publication of the Web Ecology Project (2009)
Levenshtein, I.V.: Binary codes capable of correcting deletions, insertions, and reversals. Cybernetics and Control Theory 10(8), 707–710 (1966)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical report, Stanford Digital Library Technologies Project (1998)
Weng, J., Lim, E., Jiang, J., He, Q.: Twitterrank: Finding topic-sensitive influential twitterers. In: WSDM (2010)
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Yamaguchi, Y., Takahashi, T., Amagasa, T., Kitagawa, H. (2010). TURank: Twitter User Ranking Based on User-Tweet Graph Analysis. In: Chen, L., Triantafillou, P., Suel, T. (eds) Web Information Systems Engineering – WISE 2010. WISE 2010. Lecture Notes in Computer Science, vol 6488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17616-6_22
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DOI: https://doi.org/10.1007/978-3-642-17616-6_22
Publisher Name: Springer, Berlin, Heidelberg
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