Publication:

Political polarization of news media and influencers on Twitter in the 2016 and 2020 US presidential elections

Date

Date

Date
2023
Journal Article
Published version
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Flamino, J., Galeazzi, A., Feldman, S., Macy, M. W., Cross, B., Zhou, Z., Serafino, M., Bovet, A., Makse, H. A., & Szymanski, B. K. (2023). Political polarization of news media and influencers on Twitter in the 2016 and 2020 US presidential elections. Nature Human Behaviour, 7, 904–916. https://doi.org/10.1038/s41562-023-01550-8

Abstract

Abstract

Abstract

Social media has been transforming political communication dynamics for over a decade. Here using nearly a billion tweets, we analyse the change in Twitter’s news media landscape between the 2016 and 2020 US presidential elections. Using political bias and fact-checking tools, we measure the volume of politically biased content and the number of users propagating such information. We then identify influencers—users with the greatest ability to spread news in the Twitter network. We observe that the fraction of fake and extremely biase

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Citations

57 in Web of Science Acq. date: 2025-07-27
64 in Scopus Acq. date: 2025-06-21

Additional indexing

Creators (Authors)

  • Flamino, James
    affiliation.icon.alt
  • Galeazzi, Alessandro
    affiliation.icon.alt
  • Feldman, Stuart
    affiliation.icon.alt
  • Macy, Michael W
    affiliation.icon.alt
  • Cross, Brendan
    affiliation.icon.alt
  • Zhou, Zhenkun
    affiliation.icon.alt
  • Serafino, Matteo
    affiliation.icon.alt
  • Bovet, Alexandre  orcid-logo
    affiliation.icon.alt
  • Makse, Hernán A
    affiliation.icon.alt
  • Szymanski, Boleslaw K
    affiliation.icon.alt

Journal/Series Title

Journal/Series Title

Journal/Series Title
Nature Human Behaviour

Volume

Volume

Volume
7

Number

Number

Number
6

Page range/Item number

Page range/Item number

Page range/Item number
904

Page end

Page end

Page end
916

Item Type

Item Type

Item Type
Journal Article

In collections

Publications of Institute of Mathematics
Publications of Digital Society Initiative

Dewey Decimal Classifikation

Dewey Decimal Classifikation

Dewey Decimal Classifikation
510 Mathematics

Keywords

Behavioral Neuroscience, Experimental and Cognitive Psychology, Social Psychology

Language

Language

Language
English

Publication date

Publication date

Publication date
2023-03-13

Date available

Date available

Date available
2023-06-29

Publisher

Publisher

Publisher
Nature Publishing Group

ISSN or e-ISSN

ISSN or e-ISSN

ISSN or e-ISSN
2397-3374

Additional Information

Additional Information

Additional Information
Funding text J.F., B.C. and B.K.S. were partially supported by DARPA-INCAS under Agreement No. HR001121C0165 and by NSF Grant No. BSE-2214216. H.A.M. was supported by NSF Grant No. BSE-2214217. Z.Z. was supported by the R&D Program of Beijing Municipal Education Commission, Grant No. KM202210038002. M.W.M. was partially supported by NSF Grant No. SES-2049207. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Supplementary Text Definitions of Polarization We acknowledge that political scientists distinguish multiple types of polarization [36, 37, 38, 39, 40, 41, 42, 43, 44]: affective polarization (the penchant for one partisan political group to experience animus toward an opposing partisan group), policy polarization (extreme differences of opinion on highly salient issues), partisan polarization (a substantive and affective division based on identification with opposing political parties), ideological polarization (a substantive and affective division based on identification with opposing ideological camps, e.g., liberals versus conservatives), and geographic polarization (the regional alignment of opinions, e.g., “red state/blue state”). Furthermore, each of these five types of polarization can, in turn, be classified by level: elite polarization among political officials and pundits, media polarization among news organizations, and voter polarization among the underlying population as usually measured by exit polls and opinion surveys. In the main manuscript, we seek to explore polarization to quantify the various ways the Twitter communities disseminate news. Accordingly, we opt to define polarization in the main manuscript as the growth in ideological separation between Twitter users as characterized by the political alignment of the content they propagate. Supplementary Information: Supplementary text, figs. 1–10 and tables 1–10. https://static-content.springer.com/esm/art%3A10.1038%2Fs41562-023-01550-8/MediaObjects/41562_2023_1550_MOESM1_ESM.pdf

OA Status

OA Status

OA Status
Hybrid

Publisher DOI

https://doi.org/10.1038/s41562-023-01550-8

PubMed ID

PubMed ID

PubMed ID
36914806

Metrics

Citations

57 in Web of Science Acq. date: 2025-07-27
64 in Scopus Acq. date: 2025-06-21

Citations

Citation copied

Flamino, J., Galeazzi, A., Feldman, S., Macy, M. W., Cross, B., Zhou, Z., Serafino, M., Bovet, A., Makse, H. A., & Szymanski, B. K. (2023). Political polarization of news media and influencers on Twitter in the 2016 and 2020 US presidential elections. Nature Human Behaviour, 7, 904–916. https://doi.org/10.1038/s41562-023-01550-8

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https://doi.org/10.5167/uzh-234355

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ZORA_s41562_023_01550_8.pdfview fileDownload PDF|1Download2.39 MB
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Language:eng
Licence:
CC BY 4.0
ZORA_s41562_023_01550_8.pdfview fileDownload PDF|1Download2.39 MB
Content:Published Version
Language:eng
Licence:
CC BY 4.0

Files

Files

Files
Files available to download:1
ZORA_s41562_023_01550_8.pdfview fileDownload PDF|1Download2.39 MB
Content:Published Version
Language:eng
Licence:
CC BY 4.0
ZORA_s41562_023_01550_8.pdfview fileDownload PDF|1Download2.39 MB
Content:Published Version
Language:eng
Licence:
CC BY 4.0
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