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Multidimensional Outlier Detection in Interaction Data: Application to Political Communication on Twitter

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 behaviour, 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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https://hal.archives-ouvertes.fr/hal-02085401
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Submitted on : Saturday, March 30, 2019 - 6:14:03 PM
Last modification on : Tuesday, March 23, 2021 - 9:28:02 AM

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Audrey Wilmet, Robin Lamarche-Perrin. Multidimensional Outlier Detection in Interaction Data: Application to Political Communication on Twitter. International Conference on Complex Networks, Mar 2019, Tarragona, Spain. pp.147-155, ⟨10.1007/978-3-030-14459-3_12⟩. ⟨hal-02085401⟩

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