| Abstract |
Twitter is a social networking service in which
users can create short messages related to a wide variety
of subjects. Certain subjects are highlighted by Twitter as
the most popular subjects and are known as trending topics.
In this paper we study the visual representation of these
trending topics so as to maximize the information toward
the users in the most effective way. For this purpose, we
present a new visual representation of the trending topics based
on dynamic squarified treemaps. In order to use this visual
representation, one needs to determine (preferably forecast)
the speed at which tweets on a particular subject are posted
and to detect acceleration. Moreover, one needs efficient ways
to relate topics to each other when necessary, so that clusters of
related trending topics are formed to be more informative about
a particular subject. We will outline the methodologies for
determining the speed and acceleration, and for clustering. We
present the final visual representation and discuss the benefits
of this approach over other visualization techniques. |