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Cited by 1,151Social Media and PoliticsMedia Influence and PoliticsOpinion Dynamics and Social Influence

Echo Chamber or Public Sphere? Predicting Political Orientation and Measuring Political Homophily in Twitter Using Big Data: Political Homophily on Twitter

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Colleoni, Elanor, Rozza, Alessandro, Arvidsson, Adam·2014·Journal of Communication
DOI 10.1111/jcom.12084
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Abstract

This paper investigates political homophily on Twitter. Using a combination of machine learning and social network analysis we classify users as Democrats or as Republicans based on the political content shared. We then investigate political homophily both in the network of reciprocated and nonreciprocated ties. We find that structures of political homophily differ strongly between Democrats and Republicans. In general, Democrats exhibit higher levels of political homophily. But Republicans who follow official Republican accounts exhibit higher levels of homophily than Democrats. In addition, levels of homophily are higher in the network of reciprocated followers than in the nonreciprocated network. We suggest that research on political homophily on the Internet should take the political culture and practices of users seriously. © 2014 International Communication Association.

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46 of 46 references verified0 citation errors · 0 not checkable

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  • These findings indicate there may be an in-group advantage for moral contagion; moral-emotional language may spread more widely within in-group networks than out-group networks.

    “These findings indicate there may be an in-group advantage (22,33) for moral contagion; that is, moral-emotional language may spread more widely within in-group networks than out-group networks”

    From Emotion shapes the diffusion of moralized content in social networks (2017) · cites this paper as [33]

  • Studies on social media have found limited evidence for homogeneous clusters on most social media platforms; users may be unlikely to rebroadcast opposing content but are often exposed to it and may respond.

    “Studies on social media have found limited evidence for the described homogeneous clusters on most social media platforms: while users may be unlikely to rebroadcast content from the opposing side, they are often exposed to it and may even respond (11, 62–69).”

    From How digital media drive affective polarization through partisan sorting (2022) · cites this paper as [65]

  • User leaning was also estimated by activity in specific groups, by semantics in the posts, calculation of political slant of visited websites by the user, and shared news URLs with news outlets leaning score.

    “User leaning was also estimated by activity in specific groups (e.g., [59,127,143]), by semantics in the posts (e.g., [94,99,113]), calculation of political slant of visited websites by the user [124], shared news URLs with news outlets leaning score (e.g., [8,96,129]).”

    From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [113]

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Authors
Colleoni, E., Rozza, A., & Arvidsson, A.
Affiliations
  • University of Milan (IT)
  • Parthenope University of Naples (IT)
ORCID
Venue
Journal of Communication
Publisher
Oxford University Press (OUP)
Published
2014-03-19
Volume
64
Issue
2
Pages
317-332
ISSN
0021-9916 (ISSN-L)
DOI
10.1111/jcom.12084
OpenAlex
W1908686216
Cited by
1,151
References
46
Citations by year
2026: 63 · 2025: 53 · 2024: 79 · 2023: 108 · 2022: 111 · 2021: 182 · 2020: 160 · 2019: 146 · 2018: 124 · 2017: 65 · 2016: 36 · 2015: 22 · 2014: 2
FWCI
122.54
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Communication (publishedVersion)
  • BOA (University of Milano-Bicocca) (submittedVersion, other-oa)
  • Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, other-oa)
Topics
  • Social Sciences › Communication › Social Media and Politics
  • Social Sciences › Sociology and Political Science › Media Influence and Politics
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
Keywords
political homophily, Twitter, political orientation, partisanship, Democrats and Republicans, political culture, echo chambers, public sphere, social network analysis, machine learning classification

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