Unfolding the dimensionality structure of social networks in ideological embeddings

Pedro Ramaciotti Morales (1) , Jean-Philippe Cointet (1) , Gabriel Muñoz Zolotoochin (2)

1 médialab - médialab (Sciences Po)
2 UC - Pontificia Universidad Católica de Chile

Traditionally, the opinion of people on different issues of public debate has been studied through polls and surveys. Recent advancements in network ideological scaling methods, however, have shown that digital behavioral traces in social media platforms can be used to mine opinions at a massive scale. Yet this has been shown to work in the US for one-dimensional opinion scales, best suited for two-party systems and binary social divides. In this article, we use multidimensional ideological scaling together with referential attitudinal data for some nodes. We show that opinions can be mined in a multitude of issues from social networks, embedding them in ideological spaces where dimensions stand for indicators of positive and negative opinions towards issues of public debate. This method does not require text analysis and is thus language independent. We illustrate this approach on the Twitter follower network of French users leveraging political survey data.

Mots clés: Network scaling; graph embedding; ideology; attitudinal data; party systems

Domaines: Sociologie; Réseaux sociaux et d'information [cs.SI]; Science politique

Pedro Ramaciotti Morales, Jean-Philippe Cointet, Gabriel Muñoz Zolotoochin. Unfolding the dimensionality structure of social networks in ideological embeddings. 2021. ⟨hal-03315759v1⟩