What can be learned from the multi-level reconstruction of online political landscapes dynamics? The French 2017 Presidential Elections through the lens of Twitter data.
- David Chavalarias (Centre d'Analyses de Mathématiques Sociales (CAMS), France)
We will show how to qualify and quantify the activity of political communities in a multi-polar political environment as well as their temporal evolution through the study of their digital traces.
From the analysis of a corpora of 60 million Twitter exchanges between more than 2.4 million users who interacted with political figures during the 2017 French presidential elections, we characterize the socio-semantic networks of the French political environment, as well as their development over a period of eleven months preceding the election.
This reconstruction provides unprecedented insight into the opinion dynamics and the reconfigurations of political communities, giving access to an intermediate level of resolution, between traditional sociological surveys and large statistical studies (such as those conducted by national or international organizations).
We will show how this type of reconstruction, that are intended to constitute input for social systems modeling, can provide insights into some important societal issues.