Quantifying ideological polarization on a network using generalized Euclidean distance

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Quantifying ideological polarization on a network using generalized Euclidean distance. / Hohmann, Marilena; Devriendt, Karel; Coscia, Michele.

I: Science Advances, Bind 9, Nr. 9, eabq2044, 03.03.2023.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Hohmann, M, Devriendt, K & Coscia, M 2023, 'Quantifying ideological polarization on a network using generalized Euclidean distance', Science Advances, bind 9, nr. 9, eabq2044. https://doi.org/10.1126/sciadv.abq2044

APA

Hohmann, M., Devriendt, K., & Coscia, M. (2023). Quantifying ideological polarization on a network using generalized Euclidean distance. Science Advances, 9(9), [eabq2044]. https://doi.org/10.1126/sciadv.abq2044

Vancouver

Hohmann M, Devriendt K, Coscia M. Quantifying ideological polarization on a network using generalized Euclidean distance. Science Advances. 2023 mar. 3;9(9). eabq2044. https://doi.org/10.1126/sciadv.abq2044

Author

Hohmann, Marilena ; Devriendt, Karel ; Coscia, Michele. / Quantifying ideological polarization on a network using generalized Euclidean distance. I: Science Advances. 2023 ; Bind 9, Nr. 9.

Bibtex

@article{1259841bf9824b138a5609f7f8af0653,
title = "Quantifying ideological polarization on a network using generalized Euclidean distance",
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{\textquoteright}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.",
author = "Marilena Hohmann and Karel Devriendt and Michele Coscia",
year = "2023",
month = mar,
day = "3",
doi = "10.1126/sciadv.abq2044",
language = "English",
volume = "9",
journal = "Science advances",
issn = "2375-2548",
publisher = "American Association for the Advancement of Science",
number = "9",

}

RIS

TY - JOUR

T1 - Quantifying ideological polarization on a network using generalized Euclidean distance

AU - Hohmann, Marilena

AU - Devriendt, Karel

AU - Coscia, Michele

PY - 2023/3/3

Y1 - 2023/3/3

N2 - 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.

AB - 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.

U2 - 10.1126/sciadv.abq2044

DO - 10.1126/sciadv.abq2044

M3 - Journal article

C2 - 36857460

VL - 9

JO - Science advances

JF - Science advances

SN - 2375-2548

IS - 9

M1 - eabq2044

ER -

ID: 347298222