Cited by 220Electoral Systems and Political ParticipationMedia Influence and PoliticsSocial Media and Politics

Polarization in the contemporary political and media landscape

Wilson, Anne E, Parker, Victoria A, Feinberg, Matthew·2020·Current Opinion in Behavioral Sciences

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Crossref · 10.1016/j.cobeha.2020.07.005

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65 of 65 references verified0 citation errors · 0 not checkable

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  • Scholars have made a conceptual distinction between issue polarization (an ideological, policy-based political divide) and affective polarization (dislike, distrust, and general animosity of political partisans toward the other political side).

    “Scholars have made the conceptual distinction between issue polarization—an ideological, policy-based political divide, and affective polarization, i.e. dislike, distrust, and general animosity of political partisans toward the other political side (5, 61, 62).”

    From Troll and divide: the language of online polarization (2022) · cites this paper as [62]

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Authors
Wilson, A. E., Parker, V. A., & Feinberg, M.
Affiliations
  • Wilfrid Laurier University (CA)
  • University of Toronto (CA)
Corresponding author
Wilson, Anne E
Venue
Current Opinion in Behavioral Sciences
Publisher
Elsevier BV
Published
2020-08
Volume
34
Pages
223-228
ISSN
2352-1546 (ISSN-L)
OpenAlex
W3058235320
Cited by
220
References
64
Citations by year
2026: 32 · 2025: 49 · 2024: 56 · 2023: 33 · 2022: 32 · 2021: 17 · 2020: 1
FWCI
21.01
Type
journal article
Language
en
Open access
closed
Locations
Current Opinion in Behavioral Sciences (publishedVersion)
Funders
Social Sciences and Humanities Research Council of Canada
Topics
  • Social Sciences › Political Science and International Relations › Electoral Systems and Political Participation
  • Social Sciences › Sociology and Political Science › Media Influence and Politics
  • Social Sciences › Communication › Social Media and Politics
Keywords
political polarization, affective polarization, ideological polarization, political elites, partisan media, social media dynamics

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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