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Twitter: temporal events analysis: Extended 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.