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

Audrey Wilmet (1) , Robin Lamarche-Perrin (1, 2)

1 ComplexNetworks
2 ISC-PIF - Institut des Systèmes Complexes - Paris Ile-de-France

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.

Domaines: Informatique [cs]; Réseaux et télécommunications [cs.NI]; Réseaux sociaux et d'information [cs.SI]

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⟩