  Topic lifecycle analysis on Twitter, a branch of study that investigates
Twitter topics from their birth through lifecycle to death, has gained immense
mainstream research popularity. In the literature, topics are often treated as
one of (a) hashtags (independent from other hashtags), (b) a burst of keywords
in a short time span or (c) a latent concept space captured by advanced text
analysis methodologies, such as Latent Dirichlet Allocation (LDA). The first
two approaches are not capable of recognizing topics where different users use
different hashtags to express the same concept (semantically related), while
the third approach misses out the user's explicit intent expressed via
hashtags. In our work, we use a word embedding based approach to cluster
different hashtags together, and the temporal concurrency of the hashtag
usages, thus forming topics (a semantically and temporally related group of
hashtags).We present a novel analysis of topic lifecycles with respect to
communities. We characterize the participation of social communities in the
topic clusters, and analyze the lifecycle of topic clusters with respect to
such participation. We derive first-of-its-kind novel insights with respect to
the complex evolution of topics over communities and time: temporal morphing of
topics over hashtags within communities, how the hashtags die in some
communities but morph into some other hashtags in some other communities (that,
it is a community-level phenomenon), and how specific communities adopt to
specific hashtags. Our work is fundamental in the space of topic lifecycle
modeling and understanding in communities: it redefines our understanding of
topic lifecycles and shows that the social boundaries of topic lifecycles are
deeply ingrained with community behavior.
