  A key challenge in mining social media data streams is to identify events
which are actively discussed by a group of people in a specific local or global
area. Such events are useful for early warning for accident, protest, election
or breaking news. However, neither the list of events nor the resolution of
both event time and space is fixed or known beforehand. In this work, we
propose an online spatio-temporal event detection system using social media
that is able to detect events at different time and space resolutions. First,
to address the challenge related to the unknown spatial resolution of events, a
quad-tree method is exploited in order to split the geographical space into
multiscale regions based on the density of social media data. Then, a
statistical unsupervised approach is performed that involves Poisson
distribution and a smoothing method for highlighting regions with unexpected
density of social posts. Further, event duration is precisely estimated by
merging events happening in the same region at consecutive time intervals. A
post processing stage is introduced to filter out events that are spam, fake or
wrong. Finally, we incorporate simple semantics by using social media entities
to assess the integrity, and accuracy of detected events. The proposed method
is evaluated using different social media datasets: Twitter and Flickr for
different cities: Melbourne, London, Paris and New York. To verify the
effectiveness of the proposed method, we compare our results with two baseline
algorithms based on fixed split of geographical space and clustering method.
For performance evaluation, we manually compute recall and precision. We also
propose a new quality measure named strength index, which automatically
measures how accurate the reported event is.
