Title: Dynamic time warping distance for message propagation classification in Twitter,
Abstract: Social messages classification is a research domain that has attracted the
attention of many researchers in these last years. Indeed, the social message
is different from ordinary text because it has some special characteristics
like its shortness. Then the development of new approaches for the processing
of the social message is now essential to make its classification more
efficient. In this paper, we are mainly interested in the classification of
social messages based on their spreading on online social networks (OSN). We
proposed a new distance metric based on the Dynamic Time Warping distance and
we use it with the probabilistic and the evidential k Nearest Neighbors (k-NN)
classifiers to classify propagation networks (PrNets) of messages. The
propagation network is a directed acyclic graph (DAG) that is used to record
propagation traces of the message, the traversed links and their types. We
tested the proposed metric with the chosen k-NN classifiers on real world
propagation traces that were collected from Twitter social network and we got
good classification accuracies.