Dynamic time warping distance for message propagation classification in Twitter
This work addresses the classification of short social messages for researchers and practitioners in social media analysis, but it is incremental as it adapts an existing distance metric to a specific domain.
The authors tackled the problem of classifying social messages based on their propagation patterns in online social networks by proposing a new distance metric based on Dynamic Time Warping and using it with k-NN classifiers, achieving good classification accuracies on real Twitter data.
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.