  Online social post streams such as Twitter timelines and forum discussions
have emerged as important channels for information dissemination. They are
noisy, informal, and surge quickly. Real life events, which may happen and
evolve every minute, are perceived and circulated in post streams by social
users. Intuitively, an event can be viewed as a dense cluster of posts with a
life cycle sharing the same descriptive words. There are many previous works on
event detection from social streams. However, there has been surprisingly
little work on tracking the evolution patterns of events, e.g., birth/death,
growth/decay, merge/split, which we address in this paper. To define a tracking
scope, we use a sliding time window, where old posts disappear and new posts
appear at each moment. Following that, we model a social post stream as an
evolving network, where each social post is a node, and edges between posts are
constructed when the post similarity is above a threshold. We propose a
framework which summarizes the information in the stream within the current
time window as a ``sketch graph'' composed of ``core'' posts. We develop
incremental update algorithms to handle highly dynamic social streams and track
event evolution patterns in real time. Moreover, we visualize events as word
clouds to aid human perception. Our evaluation on a real data set consisting of
5.2 million posts demonstrates that our method can effectively track event
dynamics in the whole life cycle from very large volumes of social streams on
the fly.
