A multilevel network approach to revealing patterns of online political selective exposure. Zhang, Y., Castro, L., Esser, F., & Bovet, A. PLOS One, 20(9):e0332663, September, 2025. ![A multilevel network approach to revealing patterns of online political selective exposure [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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 hierarchical clustering of political influencers, where each cluster is more frequently co-followed by a specific subgroup of survey respondents compared to others. Different indices of selective exposure, such as Community Overlap, Identity Diversity, Information Diversity, Structural Integration, and Connectivity Inequality, suggest that the characteristics of the influencer communities engaged by survey respondents vary with the level of community resolution. This finding indicates that online political selective exposure exhibits a more complex structure than a mere left-right dichotomy. Moreover, depending on the resolution level we consider, we find different associations between network indices of exposure patterns and 189 individual attributes of the survey respondents. For example, at finer levels, survey respondents’ Community Overlap is associated with several factors, such as ideological position, demographics, news consumption frequency, and incivility perception. In comparison, only their ideological position is a significant factor at coarser levels. Our work demonstrates that measuring selective exposure at a single level, such as left and right, misses important information necessary to capture this phenomenon correctly.
@article{zhangMultilevelNetworkApproach2025,
title = {A multilevel network approach to revealing patterns of online political selective exposure},
volume = {20},
copyright = {Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License},
issn = {1932-6203},
url = {https://dx.plos.org/10.1371/journal.pone.0332663},
doi = {10.1371/journal.pone.0332663},
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 hierarchical clustering of political influencers, where each cluster is more frequently co-followed by a specific subgroup of survey respondents compared to others. Different indices of selective exposure, such as Community Overlap, Identity Diversity, Information Diversity, Structural Integration, and Connectivity Inequality, suggest that the characteristics of the influencer communities engaged by survey respondents vary with the level of community resolution. This finding indicates that online political selective exposure exhibits a more complex structure than a mere left-right dichotomy. Moreover, depending on the resolution level we consider, we find different associations between network indices of exposure patterns and 189 individual attributes of the survey respondents. For example, at finer levels, survey respondents’ Community Overlap is associated with several factors, such as ideological position, demographics, news consumption frequency, and incivility perception. In comparison, only their ideological position is a significant factor at coarser levels. Our work demonstrates that measuring selective exposure at a single level, such as left and right, misses important information necessary to capture this phenomenon correctly.},
language = {en},
number = {9},
urldate = {2025-09-23},
journal = {PLOS One},
author = {Zhang, Yuan and Castro, Laia and Esser, Frank and Bovet, Alexandre},
editor = {Rivas-de-Roca, Rubén},
month = sep,
year = {2025},
keywords = {computational social science, network science, polarization, social media},
pages = {e0332663},
}
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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 hierarchical clustering of political influencers, where each cluster is more frequently co-followed by a specific subgroup of survey respondents compared to others. Different indices of selective exposure, such as Community Overlap, Identity Diversity, Information Diversity, Structural Integration, and Connectivity Inequality, suggest that the characteristics of the influencer communities engaged by survey respondents vary with the level of community resolution. This finding indicates that online political selective exposure exhibits a more complex structure than a mere left-right dichotomy. Moreover, depending on the resolution level we consider, we find different associations between network indices of exposure patterns and 189 individual attributes of the survey respondents. For example, at finer levels, survey respondents’ Community Overlap is associated with several factors, such as ideological position, demographics, news consumption frequency, and incivility perception. In comparison, only their ideological position is a significant factor at coarser levels. 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