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Public opinion on Twitter? How vote choice and arguments on Twitter comply with patterns in survey data, evidence from the 2016 Ukraine referendum in the Netherlands

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Extensive research has been done on how social media have changed democratic society, politics, and public opinion. Social media are often regarded as a mirror of the public that, during political events, provides journalists and academics with a clear image of what position the public has on political issues and which sub-issues it uses to back it up. Yet, there is strong empirical evidence that active Twitter users differ in terms of background characteristics from the electorate, and that the most influential users possess specific traits. However, this does not necessarily mean that the opinions expressed on Twitter cannot reflect public opinion. This study aims to compare sub-issues used on Twitter to polled public opinion data in the context of the 2016 so-called Ukraine referendum in the Netherlands. Our main findings indicate that there is a remarkable resemblance between the two domains in terms of sub-issues used and prominence of these sub-issues. Yet, this is mostly the case when not taking duplicates or retweets into account. Overall, the Twitter debate showed to be less nuanced than the polled public opinion data, as fewer sub-issues appeared.

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  1. This figure corresponds closely with recent statistics from Newman et al. (2019) on political social media use in the Netherlands (33%). These percentages are likely to be an overrepresentation of the true percentage as Internet users are most probably overrepresented in these surveys.

  2. To check whether this affected our results, we ran our analysis on a weighted dataset to correct for the overrepresentation of this sub-group. The results remained intact and highly compatible.

  3. The lowest one of .5 relates to the ‘strategic vote’ sub-issue, which was categorized only twice. In both cases, a different duo of coders categorized it as such, which indicates that this was not a case of disagreement, but of a missed identification. For this reason and because it is not one of the overlapping themes (this category will not be used in most of the analyses), we think that the low inter-coder reliability is not problematic.

  4. The excluded categories are “sub-issues related to the Ukrainian civil war,” and “sub-issue related to turnout of the referendum.”

  5. All scripts are available at [URL MASKED FOR BLIND PEER REVIEW].

  6. In the survey we asked voters whether they voted in favor- or against the treaty. But we also asked people why they did not vote or vote blanc, which naturally means the range of topics is much broader in the survey than the Twitter data. An overview of the relevance of all topics In the survey- compared to the Twitter data can be viewed in Figure B1 of appendix B in Electronic supplementary material.

  7. One may object to our analysis, given that our survey was slightly biased with regard to age and outcome. As an additional robustness check, we therefore carried weighing for these variables. If we weigh for age (effectively correcting for the overrepresentation of respondents between 50 and 70 in our sample), we find a very similar pattern as without (see Appendix C Figure C1 in Electronic supplementary material), leading to very few changes. We see that correcting for the age distribution does not change our conclusion. Similarly, correcting for the vote distribution does not change the general picture either (see Appendix C Figure C2 in Electronic supplementary material). While the absolute numbers of mentioned sub-issues change slightly, the relative importance does not. In any scenario, the topics Corruption, EU, and Finance form the top 3 in the survey data and are virtually indistinguishable.

  8. There are some more interesting observations to be made. For instance, while someone who followed the political developments closely may have expected that the role of former heroes of the Maidan revolution (as part of the Ukrainian people sub-issue), or the geopolitical stakes of Russia (as part of the Russia sub-issue) may have dominated the discourse, this does not seem to be the case.


  • Anstead, Nick, and Ben O'Loughlin. 2015. Social Media Analysis and Public Opinion: The 2010 UK General Election. Journal of Computer-Mediated Communication 20 (2): 204–220.

    Article  Google Scholar 

  • Bakker, Tom. 2013. The myth of the active online audience? (Doctoral dissertation).

  • Barbera, Pablo, and Gonzalo Rivero. 2015. Understanding the Political Representativeness of Twitter Users. Social Science Computer Review 33: 712–729.

    Article  Google Scholar 

  • Berinsky, A.J. 2017. Measuring public opinion with surveys. Annual Review of Political Science 20: 309–329.

    Article  Google Scholar 

  • Bode, Leticia, and Kajsa E. Dalrymple. 2014. Politics in 140 characters or less: Campaign communication, network interaction, and political participation on Twitter. Journal of Political Marketing.

    Article  Google Scholar 

  • Boyd, Danah. 2010. Social Network Sites as Networked Publics: Affordances, Dynamics, and Implications. In Networked Self: Identity, Community, and Culture on Social Network Sites (ed. Zizi Papacharissi), pp. 39–58.

  • Cameron, Jaclyn, and Nick Geidner. 2014. Something Old, Something, New, Something Borrowed from Something Blue: Experiments on Dual viewing TV and Twitter. Journal of Broadcasting and Electronic Media 58 (3): 400–419.

    Article  Google Scholar 

  • Dahlberg, Lincoln. 2011. Re-constructing digital democracy: An outline of four ‘positions’. New Media & Society 13: 855–872.

    Article  Google Scholar 

  • Downey, John, and Natalie Fenton. 2003. New Media, Counter Publicity and the Public Sphere. New Media and Society 5: 185–202.

    Article  Google Scholar 

  • Enli, Gunn. 2017. New Media and Politics. Annals of the International Communication Association 41 (3–4): 220–227.

    Article  Google Scholar 

  • Fuchs, Christian. 2017. Social Media: A Critical Introduction. New York: SAGE Publications Ltd.

    Google Scholar 

  • Gayo-Avello, Daniel. 2013. A Meta-Analysis of State-of-the-Art Electoral Prediction From Twitter Data. Social Science Computer Review 31: 649–679.

    Article  Google Scholar 

  • Gillmor, Dan. 2004. We the Media: Grassroots Journalism by the People, for the People. Sebastopol, CA: O’Reilly Media Inc.

    Book  Google Scholar 

  • Goerres, Achim. 2007. Why are Older People More Likely to Vote? The Impact of Ageing on Electoral Turnout in Europe. The British Journal of Politics and International Relations 9: 90–121.

    Article  Google Scholar 

  • Green, J., and S.B. Hobolt. 2008. Owning the Issue Agenda: Party Strategies and Vote Choices in British Elections. Electoral Studies 27 (3): 460–476.

    Article  Google Scholar 

  • Habermas, Jurgen. 1989. The Structural Transformation of the Public Sphere: An Inquiry into a Category of Bourgeois Society. Cambridge: Polity.

    Google Scholar 

  • Habermas, J. 2006. Political Communication in Media Society: Does Democracy Still Enjoy an Epistemic Dimension? The Impact of Normative Theory on Empirical Research. Communication Theory 16: 411–426.

    Article  Google Scholar 

  • Heck, W. 2016. Oekraïne kan ons niets schelen. In NRC. Accessed 10 July 2018.

  • Hillygus, D.Sunshine. 2005. The Missing Link: Exploring the Relationship Between Higher Education and Political Engagement. Political Behavior 27: 25–47.

    Article  Google Scholar 

  • Hindman, Matthew. 2008. The Myth of Digital Democracy. Princeton: Princeton University Press.

    Google Scholar 

  • Internet World Stats. 2018a. Top 50 Countries with the Highest Internet Penetration Rates. Retrieved from,

  • Internet World Stats. 2018b. Internet Stats and Facebook Usage in Europe December 2017 Statistics. Retrieved from,

  • Jacobs, Kristof. 2018. Referendums in Times of Discontent. Acta Politica 53: 489–495.

    Article  Google Scholar 

  • Jacobs, Kristof, Marijn van Klingeren, Henk van der Kolk, Tom van der Meer, and Eefje Steenvoorden. 2016. Het Oekraïne-Referendum: Nationaal Referendum Onderzoek 2016. Den Haag: Ministerie van Binnenlandse Zaken en Koninkrijksrelaties.

    Google Scholar 

  • Jungherr, Andreas. 2016. Twitter Use in Election Campaigns: A Systematic Literature Review. Journal of Information Technology and Politics 13 (1): 72–91.

    Article  Google Scholar 

  • Jungherr, Andreas, Pascal Jürgens, and Harald Schoen. 2011. Why the Pirate Party Won the German Election of 2009 or The Trouble With Predictions: A Response to Tumasjan, A., Sprenger, T. O., Sander, P. G., and Welpe, I. M. “Predicting Elections With Twitter: What 140 Characters Reveal About Political Sentiment”. Social Science Computer Review 30: 229–234.

    Article  Google Scholar 

  • Kiesraad. 2016. Uitslag Referendum Associatieovereenkomst met Oekraïne Onherroepelijk.

  • Kleinnijenhuis, Jan, and Wouter Van Atteveldt. 2016. The Impact of the Explosion of EU News on Voter Choice in the 2014 EU Elections. Politics and Governance 4 (1): 104–115.

    Article  Google Scholar 

  • Kleinnijenhuis, J., W. van Atteveldt, and V. Dekkers. 2018. Partial Priming: How Issue News Shapes Issue Saliency, Which Shapes Turnout but not the Vote. Acta Politica 53 (4): 569–589.

    Article  Google Scholar 

  • Kreiss, Daniel. 2016. Seizing the Moment: The Presidential Campaigns’ Use of Twitter During the 2012 Electoral Cycle. New Media and Society 18 (8): 1473–1490.

    Article  Google Scholar 

  • Kupavskii, A., Ostroumova, L., Umnov, A., Usachev, S., Serdyukov, P., Gusev, G., & Kustarev, A. (2012, October). Prediction of Retweet Cascade Size Over Time. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management (pp. 2335–2338). New York: ACM.

  • McGregor, S.C. 2019. Social Media as Public Opinion: How Journalists Use Social Media to Represent Public Opinion. Journalism.

    Article  Google Scholar 

  • Newman, N., Fletcher, R., Kalogeropoulos, A., & Nielsen, R. 2019. Reuters Institute Digital News Report 2019 (Vol. 2019). Reuters Institute for the Study of Journalism.

  • Papacharissi, Zizi A. 2003. The Virtual Sphere: The Internet as a Public Sphere. New Media and Society 4 (1): 9–27.

    Article  Google Scholar 

  • Papacharissi, Zizi A. 2010. A Private Sphere: Democracy in a Digital Age. New York: Wiley.

    Google Scholar 

  • Papacharissi, Zizi A., and Maria de Fatima Oliveira. 2012. Affective News and Networked Publics: The Rhythms of News Storytelling on #Egypt. Journal of Communication 62 (2): 266–282.

    Article  Google Scholar 

  • Stockmann, Daniela, and Teng Luo. 2017. Which Social Media Facilitate Online Public Opinion in China? Problems of Post-Communism 64 (3–4): 189–202.

    Article  Google Scholar 

  • Trilling, D., P. Tolochko, and B. Burscher. 2017. From Newsworthiness to Shareworthiness: How to Predict News Sharing Based on Article Characteristics. Journalism & Mass Communication Quarterly 94 (1): 38–60.

    Article  Google Scholar 

  • Vaccari, Cristian, Augusto Valeriani, Pablo Bonneau Barbera, Jost Rich, T. John, Jonathan Nagler, and Joshua Tucker. 2015. Political Expression and Action on Social Media: Exploring the Relationship between Lower- and Higher- Threshold Political Activities among Twitter Users in Italy. Journal of Computer-Mediated Communication 20: 221–239.

    Article  Google Scholar 

  • Van der Brug, W. 2004. Issue Ownership and Party Choice. Electoral Studies 23 (2): 209–233.

    Article  Google Scholar 

  • Van der Veer, Neil, Boekee, Steven, Hoekstra, Hans and Peters, Oskar. 2018. Nationale Social Media Onderzoek 2018: Het Grootste Trendonderzoek van Nederland naar het Gebruik en Verwachtingen van Social Media #NSMO. Newcom Research and Consultancy B.V.

  • Wells, C., D.V. Shah, J.C. Pevehouse, J. Yang, A. Pelled, F. Boehm, J. Lukito, S. Ghosh, and J.L. Schmidt. 2016. How Trump Drove Coverage to the Nomination: Hybrid Media Campaigning. Political Communication 33: 669–676.

    Article  Google Scholar 

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Correspondence to Marijn van Klingeren.

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van Klingeren, M., Trilling, D. & Möller, J. Public opinion on Twitter? How vote choice and arguments on Twitter comply with patterns in survey data, evidence from the 2016 Ukraine referendum in the Netherlands. Acta Polit 56, 436–455 (2021).

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