Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-29T22:23:59.541Z Has data issue: false hasContentIssue false
  • English
  • Français

Fake It ‘Til You Make It: A Natural Experiment to Identify European Politicians’ Benefit from Twitter Bots

Published online by Cambridge University Press:  11 September 2020

BRUNO CASTANHO SILVA Open the ORCID record for BRUNO CASTANHO SILVA [Opens in a new window]  and
SVEN-OLIVER PROKSCH Open the ORCID record for SVEN-OLIVER PROKSCH [Opens in a new window]
BRUNO CASTANHO SILVA*
Affiliation:
University of Cologne
SVEN-OLIVER PROKSCH*
Affiliation:
University of Cologne
*
Bruno Castanho Silva, Post-Doctoral Researcher, Cologne Center for Comparative Politics, University of Cologne, [email protected].
Sven-Oliver Proksch, Professor of Political Science and Chair for European and Multilevel Politics, Cologne Center for Comparative Politics, University of Cologne, [email protected].
Get access Rights & Permissions [Opens in a new window]

Abstract

Social media giants stand accused of facilitating illegitimate interference with democratic political processes around the world. Part of this problem are malicious bots: automated fake accounts passing as humans. However, we lack a systematic understanding of which politicians benefit most from them. We tackle this question by leveraging a Twitter purge of malicious bots in July 2018 and a new dataset on Twitter activity by all members of national parliaments (MPs) in the EU in 2018. Since users had no influence on how and when Twitter purged millions of bots, it serves as an exogenous intervention to investigate whether some parties or politicians lost more followers. We find drops in follower counts concentrated among radical right politicians, in particular those with strong anti-EU discourse. This is the first set of empirical, causally identified evidence supporting the idea that the radical right benefits more from malicious bots than other party families.

Type
Letter
Information
American Political Science Review , Volume 115 , Issue 1 , February 2021 , pp. 316 - 322
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of the American Political Science Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

We would like to thank Danielle Pullan, Pit Rieger, Rebecca Kittel, Leonie Diffené, Felix Reich, Barbara Zucchi Nobre Silva, Jasmin Spekkers, Noam Himmelrath, Lennart Schürmann, and Lea Kaftan for excellent research assistance, and Leo Baccini, Jens Wäckerle, Bastian Becker, Ahmet Suerdem, participants of the PolText Conference at Waseda University in Tokyo, September 13–15, 2019, and the three anonymous reviewers for helpful comments and suggestions. All remaining errors are our own. This research has been funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy–EXC 2126/1–390838866. Replication files are available at the American Political Science Review Dataverse: https://doi.org/10.7910/DVN/PAMABU.

References

Bakker, Ryan, de Vries, Catherine, Edwards, Erica, Hooghe, Liesbet, Jolly, Seth, Marks, Gary, Polk, Jonathan, Rovny, Jan, Steenbergen, Marco, and Vachudova, Milada Anna. 2015. “Measuring Party Positions in Europe: The Chapel Hill Expert Survey Trend File, 1999–2010.” Party Politics 21 (1): 143–152.CrossRefGoogle Scholar
Barberá, Pablo. 2018. streamR: Access to Twitter Streaming API via R. R package version 0.4.3. URL: https://cran.r-project.org/web/packages/streamR.Google Scholar
Barberá, Pablo, Casas, Andreu, Nagler, Jonathan, Egan, Patrick J., Bonneau, Richard, Jost, John T., and Tucker, Joshua A.. 2019. “Who Leads? Who Follows? Measuring Issue Attention and Agenda Setting by Legislators and the Mass Public Using Social Media Data.” American Political Science Review 113 (4): 883–901.CrossRefGoogle ScholarPubMed
Bovet, Alexandre, and Makse, Hernán A.. 2019. “Influence of Fake News in Twitter during the 2016 US Presidential Election.” Nature Communications 9 (7): 1–14.Google Scholar
Castillo, Carlos, Mendoza, Marcelo, and Poblete, Barbara. 2011. “Information Credibility on Twitter.” In WWW ‘11: Proceedings of the 20th International Conference on World Wide Web, 675–684. New York: Association for Computing Machinery. https://doi.org/10.1145/1963405.1963500.CrossRefGoogle Scholar
Confessore, Nicholas, and Dance, Gabriel J. X.. 2018. “Battling Fake Accounts, Twitter to Slash Millions of Followers.” The New York Times, July 11. https://www.nytimes.com/2018/07/11/technology/twitter-fake-followers.html.Google Scholar
de Jonge, Léonie. 2019. “The Populist Radical Right and the Media in the Benelux: Friend or Foe?” The International Journal of Press/Politics 24 (2): 189–209.CrossRefGoogle Scholar
European Commission. 2019. Report on the Implementation of the Action Plan against Disinformation. Brussels: JOIN(2019) 12 final.Google Scholar
Ferrara, Emilio. 2017. “Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Election.” First Monday 22 (8): 1–33.Google Scholar
Hix, Simon, and Lord, Christopher. 1997. Political Parties in the European Union. New York: Macmillan Education.CrossRefGoogle Scholar
Hjorth, Frederik, and Adler-Nissen, Rebecca. 2019. “Ideological Asymmetry in the Reach of Pro-Russian Digital Disinformation to United States Audiences.” Journal of Communication 69 (2): 168–192.CrossRefGoogle Scholar
Jacobs, Julia. 2018. “In Twitter Purge, Top Accounts Lose Millions of Followers.” The New York Times, July 12. https://www.nytimes.com/2018/07/12/technology/twitter-followers-nyt.html.Google Scholar
Jungherr, Andreas. 2016. “Four Functions of Digital Tools in Election Campaigns: The German Case.” The International Journal of Press/Politics 21 (3): 358–377.CrossRefGoogle Scholar
Keller, Tobias R., and Kleinen von Königslöw, Katharina. 2018. “Followers, Spread the Message! Predicting the Success of Swiss Politicians on Facebook and Twitter.” Social Media + Society 4 (1): 1–11.CrossRefGoogle Scholar
Keller, Tobias R., and Klinger, Ulrike. 2019. “Social Bots in Election Campaigns: Theoretical, Empirical, and Methodological Implications.” Political Communication 36 (1): 171–189.CrossRefGoogle Scholar
Morales, Juan S. 2020. “Perceived Popularity and Online Political Dissent: Evidence from Twitter in Venezuela.” The International Journal of Press/Politics 25 (1): 5–27.CrossRefGoogle Scholar
Mudde, Cas. 2019. The Far Right Today. Cambridge: Polity.Google Scholar
Onuchowska, Agnieszka, Berndt, Donald J., and Samtani, Sagar. 2019. Rocket Ship or Blimp? Implications of Malicious Accounts Removal on Twitter. In Proceedings of the 27th European Conference on Information Systems. ECIS, Stockholm and Uppsala, Sweden, June 8–14.Google Scholar
Popa, Sebastian Adrian, Fazekas, Zoltán, Braun, Daniela, and Leidecker-Sandmann, Melanie-Marita. 2020. “Informing the Public: How Party Communication Builds Opportunity Structures.” Political Communication 37 (3): 329–349.CrossRefGoogle Scholar
Proksch, Sven-Oliver, Lowe, Will, Wäckerle, Jens, and Soroka, Stuart. 2019. “Multilingual Sentiment Analysis: A New Approach to Measuring Conflict in Legislative Speeches.” Legislative Studies Quarterly 44 (1): 97–131.CrossRefGoogle Scholar
Rauchfleisch, Adrian, and Kaiser, Jonas. 2020. “The False Positive Problem of Automatic Bot Detection in Social Science Research.” Berkman Klein Center Research Publication March (2020-3).CrossRefGoogle Scholar
Rooduijn, Matthijs. 2019. “State of the Field: How to Study Populism and Adjacent Topics? A Plea for Both More and Less Focus.” European Journal of Political Research 58 (1): 362–372.CrossRefGoogle Scholar
Roth, Yoel, and Harvey, Del. 2018. “How Twitter is Fighting Spam and Malicious Automation,” Twitter [blog], June 26. https://blog.twitter.com/official/en_us/topics/company/2018/how-twitter-is-fighting-spam-and-malicious-automation.html.Google Scholar
Shao, Chengcheng, Ciampaglia, Giovanni Luca, Varol, Onur, Yang, Kai-Cheng, Flammini, Alessandro, and Menczer, Filippo. 2018. “The Spread of Low-Credibility Content by Social Bots.” Nature Communications 9: 1–9.CrossRefGoogle ScholarPubMed
Stukal, Denis, Sanovich, Sergey, Tucker, Joshua A., and Bonneau, Richard. 2019. “For Whom the Bot Tolls: A Neural Networks Approach to Measuring Political Orientation of Twitter Bots in Russia.” SAGE Open 9 (2): 1–16.CrossRefGoogle Scholar
Twitter. 2018. “Transparency Reports: Platform Manipulation.” https://transparency.twitter.com/en/platform-manipulation.html#platform-manipulation-jan-jun-2018.Google Scholar
Vasilopoulou, Sofia. 2018. The Radical Right and Euroskepticism. In The Oxford Handbook of the Radical Right, ed. Rydgren, Jens, 122–140. Oxford: Oxford University Press.Google Scholar
Wihbey, John, Joseph, Kenneth, and Lazer, David. 2019. “The Social Silos of Journalism? Twitter, News Media and Partisan Segregation.” New Media & Society 21 (4): 815–835.CrossRefGoogle Scholar
Young, Lori, and Soroka, Stuart N.. 2012. “Affective News: The Automated Coding of Sentiment in Political Texts.” Political Communication 29 (2): 205–231.CrossRefGoogle Scholar
Supplementary material: Link

Silva and Proksch Dataset

Dataset

https://doi.org/10.7910/DVN/PAMABU
Link
Supplementary material: PDF

Silva and Proksch supplementary material

Silva and Proksch supplementary material

Download Silva and Proksch supplementary material(PDF)
PDF 385.4 KB
Submit a response

Comments

No Comments have been published for this article.