Discovery of Interesting Users in Twitter by Overlapping Propagation Paths of Retweets

In recent years, social networking services have come into wide use to people. Especially, one of micro blog services, Twitter is a significant service. Twitter user gets information by following other users whose tweets match his interest. Retweet is one of Twitter functions which spreads tweets to other users. Using retweets, one can read tweets originated by users who are not followed by him. Our goal is to discover Twitter users who retweet many tweets which match the interest. We focus on the propagation of retweets and build a graph, the Overlap Graph, which contains users who share same retweets. Finally, we validate the users appearing in the graph by checking the frequency and the content of their retweets.

[1]  Qi Gao,et al.  Analyzing user modeling on twitter for personalized news recommendations , 2011, UMAP'11.

[2]  Latifur Khan,et al.  Tweets mining using WIKIPEDIA and impurity cluster measurement , 2010, 2010 IEEE International Conference on Intelligence and Security Informatics.

[3]  Ed H. Chi,et al.  Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network , 2010, 2010 IEEE Second International Conference on Social Computing.

[4]  Hiroshi Nakagawa,et al.  ITC-UT: Tweet Categorization by Query Categorization for On-line Reputation Management , 2010, CLEF.

[5]  Nelson F. F. Ebecken,et al.  Potential collaboration discovery using document clustering and community structure detection , 2009, CIKM-CNIKM.

[6]  Danah Boyd,et al.  Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter , 2010, 2010 43rd Hawaii International Conference on System Sciences.

[7]  Matthew Michelson,et al.  Tweet Disambiguate Entities Retrieve Folksonomy SubTree Step 1 : Discover Categories Generate Topic Profile from SubTrees Step 2 : Discover Profile Topic Profile : “ English Football ” “ World Cup ” , 2010 .

[8]  Guy Melançon,et al.  Multiscale visualization of small world networks , 2003, IEEE Symposium on Information Visualization 2003 (IEEE Cat. No.03TH8714).

[9]  John Hannon,et al.  Recommending twitter users to follow using content and collaborative filtering approaches , 2010, RecSys '10.

[10]  Matthew Michelson,et al.  Tweet Disambiguate Entities Retrieve Folksonomy SubTree Step 1 : Discover Categories Generate Topic Profile from SubTrees Step 2 : Discover Profile Topic Profile : “ English Football ” “ World Cup ” , 2011 .

[11]  Srinath Srinivasa,et al.  Intelligence and Security Informatics, Pacific Asia Workshop, PAISI 2010, Hyderabad, India, June 21, 2010. Proceedings , 2010, PAISI.

[12]  Krishna P. Gummadi,et al.  Measuring User Influence in Twitter: The Million Follower Fallacy , 2010, ICWSM.

[13]  Juan-Zi Li,et al.  Understanding retweeting behaviors in social networks , 2010, CIKM.