In this paper, we propose a method of ranking recently created Twitter accounts according to their prospective popularity. Early detection of new promising accounts is useful for trend prediction, viral marketing, user recommendation, and so on. New accounts are, however, difficult to evaluate because they have not established their reputations, and we cannot apply existing link-based or other popularity-based account evaluation methods. Our method first finds "early adopters", i.e., users who often find new good information sources earlier than others. Our method then regards new accounts followed by good early adopters as promising, even if they do not have many followers now. In order to find good early adopters, we estimate the frequency of link propagation from each account, i.e., how many times the follow links from the account have been copied by its followers. If its followers have copied many of its follow links in the past, the account must be an early adopter, who find good information sources earlier than its followers. We develop a method of inferring which links are created by copying which links. One advantage of our method is that our method only uses information that can be easily obtained only by crawling neighbors of the target accounts in the current Twitter graph. We evaluated our method by an experiment on Twitter data. We chose then-new accounts from an old snapshot of Twitter, compute their ranking by our method, and compare it with the number of followers the accounts currently have. The result shows that our method produces better rankings than various baseline methods, especially for new accounts that have only a few followers.
This paper focuses on the problem of identifying influential users of micro-blogging services. Twitter, one of the most notable micro-blogging services, employs a social-networking model called "following", in which each user can choose who she wants to "follow" to receive tweets from without requiring the latter to give permission first. In a dataset prepared for this study, it is observed that (1) 72.4 of the users in Twitter follow more than 80 of their followers, and (2) 80.5 of the users have 80 of users they are following follow them back. Our study reveals that the presence of "reciprocity" can be explained by phenomenon of homophily. Based on this finding, TwitterRank, an extension of PageRank algorithm, is proposed to measure the influence of users in Twitter. TwitterRank measures the influence taking both the topical similarity between users and the link structure into account. Experimental results show that TwitterRank outperforms the one Twitter currently uses and other related algorithms, including the original PageRank and Topic-sensitive PageRank. Twitter, a microblogging service less than three years old, commands more than 41 million users as of July 2009 and is growing fast. Twitter users tweet about any topic within the 140-character limit and follow others to receive their tweets. The goal of this paper is to study the topological characteristics of Twitter and its power as a new medium of information sharing. 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. 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 [28]. 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. 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. We have analyzed the tweets of top trending topics and reported on their temporal behavior and user participation. 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. 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. Once retweeted, a tweet gets retweeted almost instantly on next hops, signifying fast diffusion of information after the 1st retweet. To the best of our knowledge this work is the first quantitative study on the entire Twittersphere and information diffusion on it.