skip to main content
10.1145/3184558.3188733acmotherconferencesArticle/Chapter ViewAbstractPublication PageswwwConference Proceedingsconference-collections
research-article
Free Access

From Alt-Right to Alt-Rechts: Twitter Analysis of the 2017 German Federal Election

Published:23 April 2018Publication History

ABSTRACT

In the 2017 German Federal elections, the "Alternative for Deutschland'', or AfD, party was able to take control of many seats in German parliament. Their success was credited, in part, to their large online presence. Like other "alt-right'' organizations worldwide, this party is tech savvy, generating a large social media footprint, especially on Twitter, which provides an ample opportunity to understand their online behavior. In this work we present an analysis of Twitter data related to the aforementioned election. We show how users self-organize into communities, and identify the themes that define those communities. Next we analyze the content generated by those communities, and the extent to which these communities interact. Despite these elections being held in Germany, we note a substantial impact from the English-speaking Twittersphere. Specifically, we note that many of these accounts appear to be from the American alt-right movement, and support the German alt-right movement.

References

  1. Hunt Allcott and Matthew Gentzkow. 2017. Social media and fake news in the 2016 election. Technical Report. National Bureau of Economic Research.Google ScholarGoogle Scholar
  2. Sophie Chou and Deb Roy. 2017. Nasty, Brutish, and Short: What Makes Election News Popular on Twitter. In ICWSM. 492--495.Google ScholarGoogle Scholar
  3. Clayton Allen Davis, Onur Varol, Emilio Ferrara, Alessandro Flammini, and Filippo Menczer. 2016. Botornot: A system to evaluate social bots. In Proceedings of the 25th International Conference Companion on World Wide Web. International World Wide Web Conferences Steering Committee, 273--274. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Rachel Ehrenberg. 2012. Social media sway: Worries over political misinformation on Twitter attract scientists' attention. Science News 182, 8 (2012), 22--25.Google ScholarGoogle ScholarCross RefCross Ref
  5. Emilio Ferrara. 2017. Disinformation and social bot operations in the run up to the 2017 French presidential election. First Monday 22, 8 (2017).Google ScholarGoogle Scholar
  6. Emilio Ferrara. 2017. Disinformation and social bot operations in the run up to the 2017 French presidential election. (2017).Google ScholarGoogle Scholar
  7. Emilio Ferrara, Onur Varol, Clayton Davis, Filippo Menczer, and Alessandro Flammini. 2016. The rise of social bots. Commun. ACM 59, 7 (2016), 96--104. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Michelle C Forelle, Philip N Howard, Andrés Monroy-Hernández, and Saiph Savage. 2015. Political bots and the manipulation of public opinion in Venezuela. (2015).Google ScholarGoogle Scholar
  9. Patrick S Forscher and Nour S Kteily. 2017. A Psychological Profile of the AltRight. PsyArXiv (10 2017).Google ScholarGoogle Scholar
  10. Gabriel Emile Hine, Jeremiah Onaolapo, Emiliano De Cristofaro, Nicolas Kourtellis, Ilias Leontiadis, Riginos Samaras, Gianluca Stringhini, and Jeremy Blackburn. 2017. Kek, Cucks, and God Emperor Trump: A Measurement Study of 4chan's Politically Incorrect Forum and Its Effects on the Web. In ICWSM. 92--101.Google ScholarGoogle Scholar
  11. William Hobbs, Lisa Friedland, Kenneth Joseph, Oren Tsur, Stefan Wojcik, and David Lazer. 2017. " Voters of the Year": 19 Voters Who Were Unintentional Election Poll Sensors on Twitter. In ICWSM. 544--547.Google ScholarGoogle Scholar
  12. Philip N Howard, Gillian Bolsover, Bence Kollanyi, Samantha Bradshaw, and Lisa-Maria Neudert. 2017. Junk News and Bots during the US Election: What Were Michigan Voters Sharing Over Twitter Technical Report. Data Memo 2017.1. Oxford, UK: Project on Computational Propaganda. Retrieved from http://comprop. oii. ox. ac. uk/2017/03/26/junk-news-and-bots-during-the-uselection-what-weremichigan-voters-sharing-over-twitter.Google ScholarGoogle Scholar
  13. Xia Hu, Jiliang Tang, Huiji Gao, and Huan Liu. 2014. Social spammer detection with sentiment information. In Data Mining (ICDM), 2014 IEEE International Conference on. IEEE, 180--189. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Ema Kuen and Mark Strembeck. 2017. An Analysis of the Twitter Discussion on the 2016 Austrian Presidential Elections. arXiv:1707.09939 (2017).Google ScholarGoogle Scholar
  15. Renaud Lambiotte, J-C Delvenne, and Mauricio Barahona. 2008. Laplacian dynamics and multiscale modular structure in networks. arXiv preprint arXiv:0812.1770 (2008).Google ScholarGoogle Scholar
  16. Fred Morstatter, Liang Wu, Tahora H Nazer, Kathleen M Carley, and Huan Liu. 2016. A new approach to bot detection: Striking the balance between precision and recall. In Advances in Social Networks Analysis and Mining (ASONAM), 2016 IEEE/ACM International Conference on. IEEE, 533--540. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Clay Ramsay, Steven Kull, Evan Lewis, Stefan Subias, et al. 2010. Misinformation and the 2010 election: A study of the US electorate. (2010).Google ScholarGoogle Scholar
  18. Natali Ruchansky, Sungyong Seo, and Yan Liu. 2017. CSI: A Hybrid Deep Model for Fake News Detection. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 797--806. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Mark Scott. 2017. In French Elections, Alt-Right Messages and Memes Don--t Translate. (5 2017). https://www.nytimes.com/2017/05/04/technology/ french-elections-alt-right-fake-news-le-pen-macron.htmlGoogle ScholarGoogle Scholar
  20. Chengcheng Shao, Giovanni Luca Ciampaglia, Onur Varol, Alessandro Flammini, and Filippo Menczer. 2017. The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 (2017).Google ScholarGoogle Scholar
  21. Greg Ver Steeg. 2017. Unsupervised Learning via Total Correlation Explanation. arXiv preprint arXiv:1706.08984 (2017).Google ScholarGoogle Scholar
  22. Onur Varol, Emilio Ferrara, Clayton A Davis, Filippo Menczer, and Alessandro Flammini. 2017. Online human-bot interactions: Detection, estimation, and characterization. arXiv preprint arXiv:1703.03107 (2017).Google ScholarGoogle Scholar
  23. Greg Ver Steeg and Aram Galstyan. 2014. Discovering structure in highdimensional data through correlation explanation. In Advances in Neural Information Processing Systems. 577--585. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Liang Wu and Huan Liu. 2018. Tracing Fake-News Footprints: Characterizing Social Media Messages by How They Propagate. (2018).Google ScholarGoogle Scholar
  25. Liang Wu, Fred Morstatter, Xia Hu, and Huan Liu. 2016. Mining misinformation in social media. Big Data in Complex and Social Networks (2016), 123--152.Google ScholarGoogle Scholar

Index Terms

  1. From Alt-Right to Alt-Rechts: Twitter Analysis of the 2017 German Federal Election

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Other conferences
          WWW '18: Companion Proceedings of the The Web Conference 2018
          April 2018
          2023 pages
          ISBN:9781450356404

          Copyright © 2018 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          International World Wide Web Conferences Steering Committee

          Republic and Canton of Geneva, Switzerland

          Publication History

          • Published: 23 April 2018

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article

          Acceptance Rates

          Overall Acceptance Rate1,899of8,196submissions,23%

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        HTML Format

        View this article in HTML Format .

        View HTML Format