Event Clustering and Classification from Social Media: Watershed-based and kernel methods

In this paper, we present the methods for event clustering and classification defined by MediaEval 2013. For event clustering, the watershed-based method with external data sources is used. Based on two main observations, the whole metadata is turned into a user-time (UT) image, so that each row of an image contains all records that belong to one user; and the records are sorted by time. For event classification, we use supervised machine learning and experiment with Support Vector Machines. We present a composite kernel to jointly learn between text and visual features. The methods prove robustness with F-measure up to 98% in challenge 1, and the composite kernel yields competitive performance across different event types in challenge 2.