Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
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VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
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HICSS '09 Proceedings of the 42nd Hawaii International Conference on System Sciences
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Proceedings of the third ACM international conference on Web search and data mining
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HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
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Proceedings of the 20th ACM international conference on Information and knowledge management
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Proceedings of the fifth ACM international conference on Web search and data mining
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ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
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Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
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International Journal of Web Engineering and Technology
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Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Followee recommendation based on text analysis of micro-blogging activity
Information Systems
A Local Method for ObjectRank Estimation
Proceedings of International Conference on Information Integration and Web-based Applications & Services
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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.