Abstract
We introduce a method which aims at getting a better understanding of how millions of interactions may result in global events. Given a set of dimensions and a context, we find different types of outliers: a user during a given hour which is abnormal compared to its usual behavior, a relationship between two users which is abnormal compared to all other relationships, etc. We apply our method on a set of retweets related to the 2017 French presidential election and show that one can build interesting insights regarding political organization on Twitter.
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Acknowledgements
This work is funded in part by the European Commission H2020 FETPROACT 2016-2017 program under grant 732942 (ODYCCEUS), by the ANR (French National Agency of Research) under grants ANR-15- E38-0001 (AlgoDiv), by the Ile-de-France Region and its program FUI21 under grant 16010629 (iTRAC).
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Wilmet, A., Lamarche-Perrin, R. (2019). Multidimensional Outlier Detection in Interaction Data: Application to Political Communication on Twitter. In: Cornelius, S., Granell Martorell, C., Gómez-Gardeñes, J., Gonçalves, B. (eds) Complex Networks X. CompleNet 2019. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-14459-3_12
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