![]() of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis. Why we twitter: understanding microblogging usage and communities. of the 27th international conference extended abstracts on Human factors in computing systems. Micro-blogging as online word of mouth branding. Social networks that matter: Twitter under the microscope. ![]() of the 13th international conference on World Wide Web. Society for Industrial and Applied Mathematics, 2003. of the 14th annual ACM-SIAM symposium on Discrete algorithms. Robust dynamic classes revealed by measuring the response function of a social system. of the 8th ACM SIGCOMM Internet Measurement Conference. Comparison of online social relations in volume vs interaction: a case study of Cyworld. of the 18th international conference on World Wide Web. A measurement-driven analysis of information propagation in the Flickr social network. of ACM SIGCOMM Internet Measurement Conference. ![]() Characterizing user behavior in online social networks. Global organization of metabolic fluxes in the bacterium escherichia coli. of the 16th international conference on World Wide Web. Analysis of topological characteristics of huge online social networking services. To the best of our knowledge this work is the first quantitative study on the entire Twittersphere and information diffusion on it. Once retweeted, a tweet gets retweeted almost instantly on next hops, signifying fast diffusion of information after the 1st retweet. A closer look at retweets reveals that any retweeted tweet is to reach an average of 1,000 users no matter what the number of followers is of the original tweet. We have classified the trending topics based on the active period and the tweets and show that the majority (over 85%) of topics are headline news or persistent news in nature. We have analyzed the tweets of top trending topics and reported on their temporal behavior and user participation. Ranking by retweets differs from the previous two rankings, indicating a gap in influence inferred from the number of followers and that from the popularity of one's tweets. In order to identify influentials on Twitter, we have ranked users by the number of followers and by PageRank and found two rankings to be similar. In its follower-following topology analysis we have found a non-power-law follower distribution, a short effective diameter, and low reciprocity, which all mark a deviation from known characteristics of human social networks. We have crawled the entire Twitter site and obtained 41.7 million user profiles, 1.47 billion social relations, 4,262 trending topics, and 106 million tweets. The goal of this paper is to study the topological characteristics of Twitter and its power as a new medium of information sharing. Twitter users tweet about any topic within the 140-character limit and follow others to receive their tweets. ![]() Twitter, a microblogging service less than three years old, commands more than 41 million users as of July 2009 and is growing fast. ![]()
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