Abstract
Dysfunctions in online social networks (e.g., echo chambers or filter bubbles) are studied by characterizing the opinion of users, for example, as Democrat- or Republican-leaning, or in continuous scales ranging from most liberal to most conservative. Recent studies have stressed the need for studying these phenomena in complex social networks in additional dimensions of social cleavage, including anti-elite polarization and attitudes towards changing cultural issues. The study of social networks in high-dimensional opinion spaces remains challenging in settings such as that of the US, both because of the dominance of a principal liberal-conservative cleavage, and because two-party political systems structure preferences of users and the tools to measure them. This article builds on embedding of social graphs in multi-dimensional ideological spaces and NLP methods to identify additional cleavage dimensions linked to cultural, policy, social, and ideological groups and preferences. Using Twitter social graph data I infer the political stance of nearly 2 million users connected to the political debate in the US for several issue dimensions of public debate. The proposed method shows that it is possible to identify several dimensions structuring social graphs, non-aligned to liberal-conservative divides and related to new emergent social cleavages. These results also shed a new light on ideological scaling methods gaining attention in many disciplines, allowing to identify and test the nature of spatial dimensions mined on social graphs.
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Acknowledgments
This work has been been funded by the “European Polarisation Observatory” (EPO) of the CIVICA Consortium, and by the French National Agency for Research (ANR) under grants ANR-19-CE38-0006 “Geometry of Public Issues” (GOPI) and ANR-18-IDEX-0001 “IdEx Université de Paris”. Data declared the 19 March 2020 and 15 July 2021 at the registry of data processing at the Fondation Nationale de Sciences Politiques (Sciences Po) in accordance with General Data Protection Regulation 2016/679 (GDPR) and Twitter policy. For further details and the respective legal notice, please visit https://medialab.sciencespo.fr/en/activities/epo/.
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Ramaciotti Morales, P. (2023). Multidimensional Online American Politics: Mining Emergent Social Cleavages in Social Graphs. In: Cherifi, H., Mantegna, R.N., Rocha, L.M., Cherifi, C., Miccichè, S. (eds) Complex Networks and Their Applications XI. COMPLEX NETWORKS 2016 2022. Studies in Computational Intelligence, vol 1077. Springer, Cham. https://doi.org/10.1007/978-3-031-21127-0_15
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DOI: https://doi.org/10.1007/978-3-031-21127-0_15
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