  Centrality is one of the most studied concepts in social network analysis.
There is a huge literature regarding centrality measures, as ways to identify
the most relevant users in a social network. The challenge is to find measures
that can be computed efficiently, and that can be able to classify the users
according to relevance criteria as close as possible to reality. We address
this problem in the context of the Twitter network, an online social networking
service with millions of users and an impressive flow of messages that are
published and spread daily by interactions between users. Twitter has different
types of users, but the greatest utility lies in finding the most influential
ones. The purpose of this article is to collect and classify the different
Twitter influence measures that exist so far in literature. These measures are
very diverse. Some are based on simple metrics provided by the Twitter API,
while others are based on complex mathematical models. Several measures are
based on the PageRank algorithm, traditionally used to rank the websites on the
Internet. Some others consider the timeline of publication, others the content
of the messages, some are focused on specific topics, and others try to make
predictions. We consider all these aspects, and some additional ones.
Furthermore, we include measures of activity and popularity, the traditional
mechanisms to correlate measures, and some important aspects of computational
complexity for this particular context.
