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Social Network Analysis and Mining
Published in:

01-12-2023 | Original Article

Inferring attitudinal spaces in social networks

Authors: Pedro Ramaciotti Morales, Jean-Philippe Cointet, Gabriel Muñoz Zolotoochin, Antonio Fernández Peralta, Gerardo Iñiguez, Armin Pournaki

Published in: Social Network Analysis and Mining | Issue 1/2023

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Abstract

Ideological scaling methods have shown that behavioral traces in social platforms can be used to mine opinions at a massive scale. Current methods exploit one-dimensional left–right opinion scales, best suited for two-party socio-political systems and binary social divides such as those observed in the US. In this article, we introduce a new method to overcome limitations of existing methods by producing multidimensional network embeddings and align them with referential attitudinal for a few nodes. This allows us to infer a larger set of opinion dimensions from social graphs, embedding users in spaces where dimensions stand for indicators of several social dimensions including (in addition to left–right cleavages) attitudes towards elites, or ecology among many other issues. Our method does not rely on text data and is thus language-independent. We illustrate this approach approach on a Twitter follower network. Finally, we show how our method allows us to analyze the opinions shared within various communities of social networks. Our analyses show that communities of users that have extreme political opinions are also more homogeneous ideologically.
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Footnotes
1
Please refer to the Acknowledgments section to consult the data registry and treatment declaration, related GPDR documents, and access to the respective legal notice.
 
4
In comparison with Fig. 8, these new validations from Fig. 12 have wider confidence intervals for the labels “Islam” and “Nationalism” for values close to 0 in the CHES Multiculturalism salience dimensions. This is due to the fact that there are very few total users in that region of attitudinal space, but that we are including them in the figure for the sake of spanning the [0,10] interval that serves as reference for those CHES dimensions.
 
5
The full definition of CHES attitudinal dimensions can be seen in the survey’s codebook: https://​www.​chesdata.​eu/​2019-chapel-hill-expert-survey.
 
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Metadata
Title
Inferring attitudinal spaces in social networks
Authors
Pedro Ramaciotti Morales
Jean-Philippe Cointet
Gabriel Muñoz Zolotoochin
Antonio Fernández Peralta
Gerardo Iñiguez
Armin Pournaki
Publication date
01-12-2023
Publisher
Springer Vienna
Published in
Social Network Analysis and Mining / Issue 1/2023
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-022-01013-4

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