1 research outputs found
Algebraic reputation model RepRank and its application to spambot detection
Due to popularity surge social networks became lucrative targets for spammers
and guerilla marketers, who are trying to game ranking systems and broadcast
their messages at little to none cost. Ranking systems, for example Twitter's
Trends, can be gamed by scripted users also called bots, who are automatically
or semi-automatically twitting essentially the same message. Judging by the
prices and abundance of supply from PR firms this is an easy to implement and
widely used tactic, at least in Russian blogosphere. Aggregative analysis of
social networks should at best mark those messages as spam or at least
correctly downplay their importance as they represent opinions only of a few,
if dedicated, users. Hence bot detection plays a crucial role in social network
mining and analysis. In this paper we propose technique called RepRank which
could be viewed as Markov chain based model for reputation propagation on
graphs utilizing simultaneous trust and anti-trust propagation and provide
effective numerical approach for its computation. Comparison with another
models such as TrustRank and some of its modifications on sample of 320000
Russian speaking Twitter users is presented. The dataset is presented as well