ArticleScience advances2023
Quantifying ideological polarization on a network using generalized Euclidean distance.
Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- Causal Interpretation of DBSCAN Algorithm: A Dynamic Modeling for Epsilon Estimation.Entropy (Basel, Switzerland) · 2026Article
- Language model estimates indicate increased outgroup animosity among a sample of politicians, journalists, and partisan users on Twitter and Reddit.Scientific reports · 2026Article
- Competitive interactions shape mammalian brain network dynamics and computation.Nature neuroscience · 2026Article
- Polarization and health-related behaviors and outcomes during the COVID-19 pandemic: a systematic review.SSM - population health · 2026Article
- Article
- Multifaceted polarization and information reliability in climate change discussions on social media platforms.Royal Society open science · 2025Article
- Charting multidimensional ideological polarization across demographic groups in the USA.Nature human behaviour · 2025Article
- How opinion variation among in-groups can skew perceptions of ideological polarization.PNAS nexus · 2025Article
- Automatic Controversy Detection Based on Heterogeneous Signed Attributed Network and Deep Dual-Layer Self-Supervised Community Analysis.Entropy (Basel, Switzerland) · 2025Article
- Unveiling the drivers of active participation in social media discourse.Scientific reports · 2025Article
- Estimating affective polarization on a social network.PloS one · 2025Article
- K-Means Based Bee Colony Optimization for Clustering in Heterogeneous Sensor Network.Sensors (Basel, Switzerland) · 2024Article
- Echo chamber formation sharpened by priority users.iScience · 2024Article
- Quantifying metadata relevance to network block structure using description length.Communications physics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An intensely debated topic is whether political polarization on social media is on the rise. We can investigate this question only if we can quantify polarization, by taking into account how extreme the opinions of the people are, how much they organize into echo chambers, and how these echo chambers organize in the network. Current polarization estimates are insensitive to at least one of these factors: They cannot conclusively clarify the opening question. Here, we propose a measure of ideological polarization that can capture the factors we listed. The measure is based on the generalized Euclidean distance, which estimates the distance between two vectors on a network, e.g., representing people's opinion. This measure can fill the methodological gap left by the state of the art and leads to useful insights when applied to real-world debates happening on social media and to data from the U.S. Congress.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.