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1 1. Twitter Twitter Twitter ( ) Twitter (trendspotter) Twitter trendspotter TRENDSPOTTER DETECTION SYSTEM FOR TWITTER Wataru Shirakihara, 1 Tetsuya Oishi, 2 Ryuzo Hasegawa, 3 Hiroshi Hujita 3 and Miyuki Koshimura 3 It is too difficult for us to find out trends with search engines. Twitter, a popular microblogging tool, has seen a lot of growth since it launched in October, Information about the trends are posted by many twitterers. If we find out trendspotters from twitterers, and follow them, we can get it more easily. Our system uses the burst detection algorithm, and we verified its effectiveness for Twitter s posts. Finaly, we succeeded in detecting the 24 trendspotters by 5277 users. SNS Twitter Twitter ( ) 3) Twitter Twitter SNS Twitter Twitter Twitter Twitter Twitter 1 Graduate School of Information Science and Electrical Engineering, Kyushu University 2 Research Institute for Information Technology, Kyushu University 3 Faculty of Information Science and Electrical Engineering, Kyushu University 1 c 2010 Information Processing Society of Japan

2 2. Twitter Twitter 2.1 Twitter Twitter( ) Twitter Twitter ( 1 ) RT ReTweet RT ReTweet RT ( 2 ) URL Twitter Twitter 140 URL URL URL 140 URL URL ( 3 ) bot (bot) JR TV ( 4 ) API 2006 Twitter Twitter API Twitter Twitter buzztter 7) buzztter Twitter buzztter ( 5 ) (#) Twitter Twitter #traindeley #traindeley # 2.3 Twitter ( SNS) Twitter Twitter Twitter Twitter Twitter 140 Twitter SNS Twitter SNS 2 c 2010 Information Processing Society of Japan

3 mixi 8) SNS RT Twitter SNS SNS Twitter SNS Twitter SNS Twitter Twitter 3. 4) Kleinberg 5) document stream document stream document ( ) Twitter 4. ( 1 ) buzztter s buzztter ( ) ( 2 ) Twitter API s ( 1 Fig. 1 Overview of the system ) ( 3 ) s ( ) 3 ( 4 ) ( t )20 20 s trendspotter ( 5 ) 1 4 trendspotter 5. trendspotter ( 1 ) (5.1 ) trendspotter ( 2 ) trendspotter (5.2 ) trendspotter ( 3 ) trendspotter (5.3 ) 3 c 2010 Information Processing Society of Japan

4 2 Fig. 2 A number of posts including the buzz word 3 Fig. 3 A number of posts including the buzz word 5.2 trendspotter 5.1 buzztter ( ) twitter WILLCOM twitter (20 ) twitter (20 ) twitter Fig. 4 A number of posts including the buzz word (20 ) (a) 4 c 2010 Information Processing Society of Japan

5 5 Fig. 5 Consideration1: (a) (a) Tue, 26 Jan :04: mubot ( WILLCOM ) ( ) (b) (c) (c) (c) Wed, 27 Jan :01: hagexx Wed, 27 Jan :06: ysbee = - Wed, 27 Jan :08: awazeno999 Wed, 27 Jan :23: takeori Wed, 27 Jan :24: tdaiki Wed, 27 Jan :26: tabloid RT ( A B ) 5 c 2010 Information Processing Society of Japan

6 6 Fig. 6 Consideration2: 7 Fig. 7 Consideration3: ( 6) (d) (e) (d) Tue, 26 Jan :10: (e) togamim Tue, 26 Jan :01: MAKEPURA NHK (d) (e) Twitter ( ) 5.2 : trendspotter trendspotter Buzztter trendspotter 20 trendspotter 6 c 2010 Information Processing Society of Japan

7 trendspotter =4000 N trendspotter (5277 ) 1 1 N trendspotter trendspotter N (34381 ) 2 Table 1 A number of trendspotters for N buzz words and percentage of total (N) (%) N Table 2 A number of users posted N buzz words in bursts and percentage of total (N) (%) trendspotter 1.52% : trendspotter trendspotter Buzztter ( ) trendspotter 24 N (24 ) 3 3 trendspotter 24 N Table 3 A number of the trendspotters posted N buzz words in bursts and percentage of total (N) (%) trendspotter 7 c 2010 Information Processing Society of Japan

8 % trendspotter 33.4% trendspotter Twitter (trendspotter) ( ) Twitter ( ) trendspotter 7. Twitter ( ) trendspotter ( ) Twitter ( IT ) ( ).. Twitter /. ( ) ( ). Hadoop. Hadoop HBase, Cassandra ) Twitter ) ) news/news htm 4) document stream burst IPSJ SIG Notes 2004(23) pp ) Jon Kleinberg Bursty and hierarchical structure in streams the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ) 15 pp ) 8) 8 c 2010 Information Processing Society of Japan

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