Behavioral differences: insights, explanations and comparisons of French and US Twitter usage during elections

Ian Davidson, Antoine Gourru, Julien Velcin, Yue Wu

Abstract: Social networks and social media have played a key role for observing and influencing how the political landscape takes shape and dynamically shifts. It is especially true in events such as national elections as indicated by earlier studies with Facebook (Williams and Gulati, in: Proceedings of the annual meeting of the American Political Science Association, 2009) and Twitter (Larsson and Moe in New Med Soc 14(5):729–747, 2012). Not surprisingly in an attempt to better understand and simplify these networks, community discovery methods have been used, such as the Louvain method (Blondel et al. in J Stat Mechanics Theory Exp 2008(10):P10008, 2008) to understand elections (Gaumont et al. in PLoS ONE 13(9):e0201879, 2018). However, most community-based studies first simplify the complex Twitter data into a single network based on (for example) follower, retweet or friendship properties. This requires ignoring some information or combining many types of information into a graph, which can mask many insights. In this paper, we explore Twitter data as a time-stamped vertex-labeled graph. The graph structure can be given by a structural relation between the users such as retweet, friendship or follower relation, whilst the behavior of the individual is given by their posting behavior which is modeled as a time-evolving vertex labels. We explore leveraging existing community discovery methods to find communities using just the structural data and then describe these communities using behavioral data. We explore two complimentary directions: (1) creating a taxonomy of hashtags based on their community usage and (2) efficiently describing the communities expanding our recently published work. We have created two datasets, one each for the French and US elections from which we compare and contrast insights on the usage of hashtags.