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Predicting Popularity of Twitter Accounts through the Discovery of Link-Propagating Early Adopters

by Daichi Imamori, Keishi Tajima

Abstract

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 yet established the reputation they deserve, 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 the frequency is high, the account must be a good early adopter who often find good information sources earlier than its followers. We develop a method of inferring which links are created by copying which links. One important 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 ranking based on the number of followers the accounts currently have. The result shows that our method produces better rankings than various baseline methods, especially for very new accounts that have only a few followers.

Full Text: free download from ACM

Slides: pdf

BibTex entry

Keywords

microblog; social network; target users; user intention; target specificity; target diversity
Published in Proc. of ACM Conference on Information and Knowledge Management, pp.639-648, Indianapois, IN, 2016


tajima@i.kyoto-u.ac.jp / Fax: +81(Japan) 75-753-5978 / Office: Research Bldg. #7, room 404