EVENT BASED FILTERING SYSTEM FOR SOCIAL MEDIA

People use social media (SM) to
describe and discuss different situations they
are involved in, like crises. It is therefore
worthwhile to exploit SM contents to
support crisis management, in particular by
revealing useful and unknown information
about the crises in real-time. Hence, we
propose a novel active online multipleprototype
classifier, called AOMPC. It
identifies relevant data related to a crisis.
AOMPC is an online learning algorithm that
operates on data streams and which is
equipped with active learning mechanisms
to actively query the label of ambiguous
unlabeled data. The number of queries is
controlled by a fixed budget strategy.
Typically, AOMPC accommodates partly
labeled data streams. AOMPC was
evaluated using two types of data:
(1) synthetic data and (2) SM data from
Twitter related to two crises, Colorado
Floods and Australia Bushfires. To provide
a thorough evaluation, a whole set of known
metrics was used to study the quality of the
results. Moreover, a sensitivity analysis was
conducted to show the effect of AOMPC’s
parameters on the accuracy of the results. A
comparative study of AOMPC against other
available online learning algorithms was
performed. The experiments showed very
good behavior of AOMPC for dealing with
evolving, partly-labeled data streams.