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Quantifying Polarization on Twitter: The Kavanaugh Nomination

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Social Informatics (SocInfo 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11864))

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Abstract

This paper addresses polarization quantification, particularly as it pertains to the nomination of Brett Kavanaugh to the US Supreme Court and his subsequent confirmation with the narrowest margin since 1881. Republican (GOP) and Democratic (DNC) senators voted overwhelmingly along party lines. In this paper, we examine political polarization concerning the nomination among Twitter users. To do so, we accurately identify the stance of more than 128 thousand Twitter users towards Kavanaugh’s nomination using both semi-supervised and supervised classification. Next, we quantify the polarization between the different groups in terms of who they retweet and which hashtags they use. We modify existing polarization quantification measures to make them more efficient and more effective. We also characterize the polarization between users who supported and opposed the nomination.

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Notes

  1. 1.

    https://www.senate.gov/pagelayout/reference/nominations/Nominations.htm.

  2. 2.

    https://en.wikipedia.org/wiki/Brett_Kavanaugh.

  3. 3.

    https://www.nytimes.com/2018/09/26/us/politics/brett-kavanaugh-accusers-women.html.

  4. 4.

    https://github.com/edsu/twarc.

  5. 5.

    Calculated using https://surveysystem.com/sscalc.htm.

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Author information

Authors and Affiliations

  1. Qatar Computing Research Institute, HBKU, Doha, Qatar

    Kareem Darwish

Authors
  1. Kareem Darwish
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Corresponding author

Correspondence to Kareem Darwish .

Editor information

Editors and Affiliations

  1. Qatar Computing Research Institute, Doha, Qatar

    Ingmar Weber

  2. Qatar Computing Research Institute, Doha, Qatar

    Kareem M. Darwish

  3. University of Koblenz-Landau, Koblenz, Germany

    Claudia Wagner

  4. Max Planck Institute for Demographic Research, Rostock, Germany

    Emilio Zagheni

  5. Northeastern University, Boston, MA, USA

    Laura Nelson

  6. Max Planck Institute for Demographic Research, Rostock, Germany

    Samin Aref

  7. GESIS-Leibniz Institute for the Social Sciences, Cologne, Germany

    Fabian Flöck

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Darwish, K. (2019). Quantifying Polarization on Twitter: The Kavanaugh Nomination. In: Weber, I., et al. Social Informatics. SocInfo 2019. Lecture Notes in Computer Science(), vol 11864. Springer, Cham. https://doi.org/10.1007/978-3-030-34971-4_13

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  • DOI: https://doi.org/10.1007/978-3-030-34971-4_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34970-7

  • Online ISBN: 978-3-030-34971-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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