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.
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Notes
- 1.
We call propagation network the network that conserves propagation traces of the message, i.e. traversed links and nodes.
- 2.
We call propagation level the number of links between the source of the message and the target node.
- 3.
Directed Acyclic Graph.
- 4.
Twitter4j is a java library for the Twitter API, it is an open-sourced software and free of charge and it was created by Yusuke Yamamoto. More details can be found in http://twitter4j.org/en/index.html.
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Acknowledgement
These research works and innovation are carried out within the framework of the device MOBIDOC financed by the European Union under the PASRI program and administrated by the ANPR. Also, we thank the “Centre d’Etude et de Recherche des Télécommunications” (CERT) for their support.
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Jendoubi, S., Martin, A., Liétard, L., Ben Yaghlane, B., Ben Hadji, H. (2015). Dynamic Time Warping Distance for Message Propagation Classification in Twitter. In: Destercke, S., Denoeux, T. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2015. Lecture Notes in Computer Science(), vol 9161. Springer, Cham. https://doi.org/10.1007/978-3-319-20807-7_38
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