Multidimensional Outlier Detection in Temporal Interaction Networks: An Application to Political Communication on Twitter

TL;DR

This paper introduces a multidimensional outlier detection method for temporal interaction networks, specifically applied to Twitter data during the 2017 French presidential election, to identify abnormal behaviors and trends.

Contribution

The paper proposes a novel approach to model Twitter interactions as a data cube and uses context-based analysis to detect unexpected behaviors across multiple dimensions.

Findings

Identified abnormal user behaviors during specific hours.

Detected unusual relationships between users.

Gained insights into political communication patterns.

Abstract

In social network Twitter, users can interact with each other and spread information via retweets. These millions of interactions may result in media events whose influence goes beyond Twitter framework. In this paper, we thoroughly explore interactions to provide a better understanding of the emergence of certain trends. First, we consider an interaction on Twitter to be a triplet $(s,a,t)$ meaning that user $s$, called the spreader, has retweeted a tweet of user $a$, called the author, at time $t$. We model this set of interactions as a data cube with three dimensions: spreaders, authors and time. Then, we provide a method which builds different contexts, where a context is a set of features characterizing the circumstances of an event. Finally, these contexts allow us to find relevant unexpected behaviors, according to several dimensions and various perspectives: 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, \textit{etc.} We apply our method to a set of retweets related to the 2017 French presidential election and show that one can build interesting insights regarding political organization on Twitter.