  Many works related to Twitter aim at characterizing its users in some way:
role on the service (spammers, bots, organizations, etc.), nature of the user
(socio-professional category, age, etc.), topics of interest , and others.
However, for a given user classification problem, it is very difficult to
select a set of appropriate features, because the many features described in
the literature are very heterogeneous, with name overlaps and collisions, and
numerous very close variants. In this article, we review a wide range of such
features. In order to present a clear state-of-the-art description, we unify
their names, definitions and relationships, and we propose a new, neutral,
typology. We then illustrate the interest of our review by applying a selection
of these features to the offline influence detection problem. This task
consists in identifying users which are influential in real-life, based on
their Twitter account and related data. We show that most features deemed
efficient to predict online influence, such as the numbers of retweets and
followers, are not relevant to this problem. However, We propose several
content-based approaches to label Twitter users as Influencers or not. We also
rank them according to a predicted influence level. Our proposals are evaluated
over the CLEF RepLab 2014 dataset, and outmatch state-of-the-art methods.
