  Contemporary social media networks can be viewed as a break to the early
two-step flow model in which influential individuals act as intermediaries
between the media and the public for information diffusion. Today's social
media platforms enable users to both generate and consume online contents.
Users continuously engage and disengage in discussions with varying degrees of
interaction leading to formation of distinct online communities. Such
communities are often formed at high-level either based on metadata, such as
hashtags on Twitter, or popular content triggered by few influential users.
These online communities often do not reflect true connectivity and lack the
cohesiveness of traditional communities. In this study, we investigate
real-time formation of temporal communities on Twitter. We aim at defining both
high and low levels connections and to reveal the magnitude of clustering
cohesion on temporal basis. Inspired by a real-life event center sitting
arrangement scenario, the proposed method aims to cluster users into distinct
and cohesive online temporal communities. Membership to a community relies on
intrinsic tweet properties to define similarity as the basis for interaction
networks. The proposed method can be useful for local event monitoring and
clique-based marketing among other applications.
