Twitter: Temporal Events Analysis: Extended Abstract

ABSTRACT
We perform a temporal analysis of the Twitter stream to investigate
the evolution of unique events based on the burst of
popularity of associated hashtags. We derive a classification
of events according to the different patterns corresponding
to the peak of the volume of exchanged message and to
how these events propagate on any social network with the
same characteristics as Twitter. We first provide a precise
definition of unique events and correlate them to hashtags.
With reference to a specific interval of time, the most popular
- with respect to number of tweets- hashtags are then
detected using the Seasonal Hybrid ESD (S-H-ESD) technique
introduced by Twitter. After identifying the unique
hashtags among the 1000 most popular, we have identified,
through an unsupervised Machine Learning algorithm applied
to the historical temporal series of hashtags limited
around the maximum peak, the temporal patterns (clusters)
of the events. Finally, using the Twitter features, for each
cluster, we have studied both the process at the origin of the
event and how they evolve over the network.