(LINATE) Language-Independent Attitudinal Embedding

Language-Independent Attitudinal Embedding

Public opinion on different issues of public debate is traditionally studied through polls and surveys. Recent advancements in network ideological scaling methods, however, show that digital behavioral traces in social platforms can be used to infer opinions at a massive scale. Current approaches allow to position social network users on ideological scales ranging extreme left- to extreme right-wing stances. This is suited for two-party systems and binary social divides, as the case of the US. However, no similar methodology exists for European (and other) settings, where public political debate is structured along several different issues and cleavages, and party systems impose a multi-polar landscape. To overcome this gap, we propose a novel approach that mixes ideological scaling of social graphs with external attitudinal. The former uses social network structures to embed users in spaces where dimensions are informative of ideological traits. The latter provide stances for a few users on attitudinal scales (from most opposed to most favorable) for a range of well-identified issues of public debate: e.g., taxation, immigration, European integration, environmental protection, civil liberties. Mixing the two, we aim at embedding a massive number of users in attitudinal spaces where positions along several dimensions indicate opinions towards identifiable issues structuring public debate.