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.
microblog;
social network;
target users;
user intention;
target specificity;
target diversity
Published in Proc. of ACM CIKM, pp.639-648, Indianapois, IN, 2016
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