IDEAS home Printed from https://ideas.repec.org/a/spr/jcsosc/v4y2021i1d10.1007_s42001-020-00080-x.html
   My bibliography  Save this article

Online influence, offline violence: language use on YouTube surrounding the ‘Unite the Right’ rally

Author

Listed:
  • Isabelle Vegt

    (University College London)

  • Maximilian Mozes

    (University College London
    University College London
    University College London)

  • Paul Gill

    (University College London)

  • Bennett Kleinberg

    (University College London
    University College London)

Abstract

The media frequently describes the 2017 Charlottesville ‘Unite the Right’ rally as a turning point for the alt-right and white supremacist movements. Social movement theory suggests that the media attention and public discourse concerning the rally may have engendered changes in social identity performance and visibility of the alt-right, but this has yet to be empirically tested. The presence of the movement on YouTube is of particular interest, as this platform has been referred to as a breeding ground for the alt-right. The current study investigates whether there are differences in language use between 7142 alt-right and progressive YouTube channels, in addition to measuring possible changes as a result of the rally. To do so, we create structural topic models and measure bigram proportions in video transcripts, spanning approximately 2 months before and after the rally. We observe differences in topics between the two groups, with the ‘alternative influencers’, for example, discussing topics related to race and free speech to a larger extent than progressive channels. We also observe structural breakpoints in the use of bigrams at the time of the rally, suggesting there are changes in language use within the two groups as a result of the rally. While most changes relate to mentions of the rally itself, the alternative group also shows an increase in promotion of their YouTube channels. In light of social movement theory, we argue that language use on YouTube shows that the Charlottesville rally indeed triggered changes in social identity performance and visibility of the alt-right.

Suggested Citation

  • Isabelle Vegt & Maximilian Mozes & Paul Gill & Bennett Kleinberg, 2021. "Online influence, offline violence: language use on YouTube surrounding the ‘Unite the Right’ rally," Journal of Computational Social Science, Springer, vol. 4(1), pages 333-354, May.
    • Handle: RePEc:spr:jcsosc:v:4:y:2021:i:1:d:10.1007_s42001-020-00080-x
    DOI: 10.1007/s42001-020-00080-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s42001-020-00080-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s42001-020-00080-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zeileis, Achim & Leisch, Friedrich & Hornik, Kurt & Kleiber, Christian, 2002. "strucchange: An R Package for Testing for Structural Change in Linear Regression Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 7(i02).
    2. Bliuc, Ana-Maria & Best, David & Iqbal, Muhammad & Upton, Katie, 2017. "Building addiction recovery capital through online participation in a recovery community," Social Science & Medicine, Elsevier, vol. 193(C), pages 110-117.
    3. Margaret Roberts & Brandon Stewart & Tingley, Dustin, 2014. "stm: R Package for Structural Topic Models," Working Paper 176291, Harvard University OpenScholar.
    4. Margaret E. Roberts & Brandon M. Stewart & Dustin Tingley & Christopher Lucas & Jetson Leder‐Luis & Shana Kushner Gadarian & Bethany Albertson & David G. Rand, 2014. "Structural Topic Models for Open‐Ended Survey Responses," American Journal of Political Science, John Wiley & Sons, vol. 58(4), pages 1064-1082, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Anna Ruelens, 2022. "Analyzing user-generated content using natural language processing: a case study of public satisfaction with healthcare systems," Journal of Computational Social Science, Springer, vol. 5(1), pages 731-749, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andreas Rehs, 2020. "A structural topic model approach to scientific reorientation of economics and chemistry after German reunification," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 1229-1251, November.
    2. Nuccio Ludovico & Federica Dessi & Marino Bonaiuto, 2020. "Stakeholders Mapping for Sustainable Biofuels: An Innovative Procedure Based on Computational Text Analysis and Social Network Analysis," Sustainability, MDPI, vol. 12(24), pages 1-22, December.
    3. Xieling Chen & Juan Chen & Gary Cheng & Tao Gong, 2020. "Topics and trends in artificial intelligence assisted human brain research," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-27, April.
    4. Jo, Wonkwang & You, Myoungsoon, 2019. "News media’s framing of health policy and its implications for government communication: A text mining analysis of news coverage on a policy to expand health insurance coverage in South Korea," Health Policy, Elsevier, vol. 123(11), pages 1116-1124.
    5. Fabrizio Gilardi & Charles R. Shipan & Bruno Wüest, 2021. "Policy Diffusion: The Issue‐Definition Stage," American Journal of Political Science, John Wiley & Sons, vol. 65(1), pages 21-35, January.
    6. Kolomoyets, Yuliya & Dickinger, Astrid, 2023. "Understanding value perceptions and propositions: A machine learning approach," Journal of Business Research, Elsevier, vol. 154(C).
    7. Ebadi, Ashkan & Tremblay, Stéphane & Goutte, Cyril & Schiffauerova, Andrea, 2020. "Application of machine learning techniques to assess the trends and alignment of the funded research output," Journal of Informetrics, Elsevier, vol. 14(2).
    8. Federica Genovese & Endre Tvinnereim, 2019. "Who opposes climate regulation? Business preferences for the European emission trading scheme," The Review of International Organizations, Springer, vol. 14(3), pages 511-542, September.
    9. Mourtgos, Scott M. & Adams, Ian T., 2019. "The rhetoric of de-policing: Evaluating open-ended survey responses from police officers with machine learning-based structural topic modeling," Journal of Criminal Justice, Elsevier, vol. 64(C), pages 1-1.
    10. Sanders, James & Lisi, Giulio & Schonhardt-Bailey, Cheryl, 2018. "Themes and topics in parliamentary oversight hearings: a new direction in textual data analysis," LSE Research Online Documents on Economics 87624, London School of Economics and Political Science, LSE Library.
    11. Nuccio Ludovico & Marc Esteve Del Valle & Franco Ruzzenenti, 2020. "Mapping the Dutch Energy Transition Hyperlink Network," Sustainability, MDPI, vol. 12(18), pages 1-24, September.
    12. Jeet Dogra & Venkata Rohan Sharma Karri, 2020. "Assessment of Luxury Trains in India: A Case Study of Maharajas’ Express," Journal of Tourismology, Istanbul University, Faculty of Economics, vol. 6(2), pages 185-200, December.
    13. Maria Lourdes Ordoñez Olivo & Zoltán Lakner, 2023. "Shaping the Knowledge Base of Bioeconomy Sectors Development in Latin American and Caribbean Countries: A Bibliometric Analysis," Sustainability, MDPI, vol. 15(6), pages 1-18, March.
    14. Dean Neu & Gregory D. Saxton & Abu S. Rahaman, 2022. "Social Accountability, Ethics, and the Occupy Wall Street Protests," Journal of Business Ethics, Springer, vol. 180(1), pages 17-31, September.
    15. Sandra Wankmüller, 2023. "A comparison of approaches for imbalanced classification problems in the context of retrieving relevant documents for an analysis," Journal of Computational Social Science, Springer, vol. 6(1), pages 91-163, April.
    16. Minchul Lee & Min Song, 2020. "Incorporating citation impact into analysis of research trends," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1191-1224, August.
    17. Magdalena Mikolajek-Gocejna, 2021. "Estimation, Instability, and Non-Stationarity of Beta Coefficients for Twenty-four Emerging Markets in 2005-2021," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 370-395.
    18. Grajzl, Peter & Murrell, Peter, 2021. "A machine-learning history of English caselaw and legal ideas prior to the Industrial Revolution I: generating and interpreting the estimates," Journal of Institutional Economics, Cambridge University Press, vol. 17(1), pages 1-19, February.
    19. Akaev, Askar & Devezas, Tessaleno & Ichkitidze, Yuri & Sarygulov, Askar, 2021. "Forecasting the labor intensity and labor income share for G7 countries in the digital age," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    20. Grün, Bettina & Kosmidis, Ioannis & Zeileis, Achim, 2012. "Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i11).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jcsosc:v:4:y:2021:i:1:d:10.1007_s42001-020-00080-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.