A multilevel network approach to revealing patterns of online political selective exposure
Yuan Zhang, Laia Castro Herrero, Frank Esser, Alexandre Bovet

TL;DR
This paper introduces a multilevel network analysis framework to better understand online political selective exposure, revealing complex hierarchical patterns beyond traditional left-right measures during the 2022 Brazilian Presidential Election.
Contribution
It presents a novel multilevel community detection approach to analyze political influencer networks, uncovering hierarchical structures and nuanced selective exposure patterns.
Findings
Selective exposure patterns vary with community resolution levels.
Finer community levels relate to demographic and ideological factors.
Coarser levels primarily associate with ideological position.
Abstract
Selective exposure, individuals' inclination to seek out information that supports their beliefs while avoiding information that contradicts them, plays an important role in the emergence of polarization and echo chambers. In the political domain, selective exposure is usually measured on a left-right ideology scale, ignoring finer details. To bridge the gap, this work introduces a multilevel analysis framework based on a multi-scale community detection approach. To test this approach, we combine survey and Twitter/X data collected during the 2022 Brazilian Presidential Election and investigate selective exposure patterns among survey respondents in their choices of whom to follow. We construct a bipartite network connecting survey respondents with political influencers and project it onto the influencer nodes. Applying multi-scale community detection to this projection uncovers a…
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Taxonomy
TopicsSocial Media and Politics
