research-article

Free access

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

Authors:

Fred Morstatter,

Shanika KarunasekeraAuthors Info & Claims

WWW '18: Companion Proceedings of the The Web Conference 2018

Pages 621 - 628

https://doi.org/10.1145/3184558.3188733

Published: 23 April 2018 Publication History

All formats PDF

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.

[2]

Sophie Chou and Deb Roy. 2017. Nasty, Brutish, and Short: What Makes Election News Popular on Twitter. In ICWSM. 492--495.

[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.

Digital Library

[4]

Rachel Ehrenberg. 2012. Social media sway: Worries over political misinformation on Twitter attract scientists' attention. Science News 182, 8 (2012), 22--25.

[5]

Emilio Ferrara. 2017. Disinformation and social bot operations in the run up to the 2017 French presidential election. First Monday 22, 8 (2017).

[6]

Emilio Ferrara. 2017. Disinformation and social bot operations in the run up to the 2017 French presidential election. (2017).

[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.

Digital 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).

[9]

Patrick S Forscher and Nour S Kteily. 2017. A Psychological Profile of the AltRight. PsyArXiv (10 2017).

[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.

[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.

[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.

[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.

Digital Library

[14]

Ema Kuen and Mark Strembeck. 2017. An Analysis of the Twitter Discussion on the 2016 Austrian Presidential Elections. arXiv:1707.09939 (2017).

[15]

Renaud Lambiotte, J-C Delvenne, and Mauricio Barahona. 2008. Laplacian dynamics and multiscale modular structure in networks. arXiv preprint arXiv:0812.1770 (2008).

[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.

Digital 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).

[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.

Digital 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.html

[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).

[21]

Greg Ver Steeg. 2017. Unsupervised Learning via Total Correlation Explanation. arXiv preprint arXiv:1706.08984 (2017).

[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).

[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.

Digital Library

[24]

Liang Wu and Huan Liu. 2018. Tracing Fake-News Footprints: Characterizing Social Media Messages by How They Propagate. (2018).

[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.

Cited By

Hartwig KSchmid SBiselli TPleil HReuter C(2024)Misleading information in crises: exploring content-specific indicators on Twitter from a user perspectiveBehaviour & Information Technology10.1080/0144929X.2024.2373166(1-34)Online publication date: 8-Jul-2024
https://doi.org/10.1080/0144929X.2024.2373166
Tardelli SNizzoli LTesconi MConti MNakov PDa San Martino GCresci S(2024)Temporal dynamics of coordinated online behavior: Stability, archetypes, and influenceProceedings of the National Academy of Sciences10.1073/pnas.2307038121121:20Online publication date: 6-May-2024
https://doi.org/10.1073/pnas.2307038121
Matos BLima RAlmeida JGonçalves MSantos R(2024)Intellectual dark web, alt-lite and alt-right: Are they really that different? a multi-perspective analysis of the textual content produced by contrariansSocial Network Analysis and Mining10.1007/s13278-023-01187-514:1Online publication date: 25-Jan-2024
https://doi.org/10.1007/s13278-023-01187-5
Show More Cited By

Index Terms

From Alt-Right to Alt-Rechts: Twitter Analysis of the 2017 German Federal Election
1. Networks
  1. Network types
    1. Overlay and other logical network structures
      1. Online social networks
      2. Social media networks
2. Social and professional topics
  1. Professional topics
    1. Computing and business
      1. Socio-technical systems

Recommendations

Measuring Extremism: Validating an Alt-Right Twitter Accounts Dataset
Intelligent Data Engineering and Automated Learning – IDEAL 2018
Abstract
Twitter is one of the most commonly used Online Social Networks in the world and it has consequently attracted considerable attention from different political groups attempting to gain influence. Among these groups is the alt-right; a modern far-...
Multidimensional Analysis of Fake News Spreaders on Twitter
Computational Data and Social Networks
Abstract
Social media has become a tool to spread false information with the help of its large complex network. The consequences of such misinformation could be very severe. The paper uses the Twitter conversations about the scrapping of Article 370 in ...
Identifying the influential bloggers in a community
WSDM '08: Proceedings of the 2008 International Conference on Web Search and Data Mining

Blogging becomes a popular way for a Web user to publish information on the Web. Bloggers write blog posts, share their likes and dislikes, voice their opinions, provide suggestions, report news, and form groups in Blogosphere. Bloggers form their ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '18: Companion Proceedings of the The Web Conference 2018

April 2018

2023 pages

ISBN:9781450356404

General Chairs:
Pierre-Antoine Champin
Université Claude Bernard Lyon 1, France
,
Fabien Gandon
Inria, Université Côte d'Azur, CNRS, I3S, France
,
Lionel Médini
Université Claude Bernard Lyon 1, CNRS, LIRIS, France
,
Program Chairs:
Mounia Lalmas
Spotify, UK
,
Panagiotis G. Ipeirotis
New York University, USA

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]

Sponsors

IW3C2: International World Wide Web Conference Committee

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 23 April 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

WWW '18

Sponsor:

IW3C2

WWW '18: The Web Conference 2018

April 23 - 27, 2018

Lyon, France

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

23
Total Citations
View Citations
1,563
Total Downloads

Downloads (Last 12 months)215
Downloads (Last 6 weeks)24

Reflects downloads up to 14 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Hartwig KSchmid SBiselli TPleil HReuter C(2024)Misleading information in crises: exploring content-specific indicators on Twitter from a user perspectiveBehaviour & Information Technology10.1080/0144929X.2024.2373166(1-34)Online publication date: 8-Jul-2024
https://doi.org/10.1080/0144929X.2024.2373166
Tardelli SNizzoli LTesconi MConti MNakov PDa San Martino GCresci S(2024)Temporal dynamics of coordinated online behavior: Stability, archetypes, and influenceProceedings of the National Academy of Sciences10.1073/pnas.2307038121121:20Online publication date: 6-May-2024
https://doi.org/10.1073/pnas.2307038121
Matos BLima RAlmeida JGonçalves MSantos R(2024)Intellectual dark web, alt-lite and alt-right: Are they really that different? a multi-perspective analysis of the textual content produced by contrariansSocial Network Analysis and Mining10.1007/s13278-023-01187-514:1Online publication date: 25-Jan-2024
https://doi.org/10.1007/s13278-023-01187-5
Cruz JQuiles M(2024)The 2018 Brazilian Presidential Run-Off: A Complex Network Analysis Approach Using Twitter DataComputational Science and Its Applications – ICCSA 202410.1007/978-3-031-64608-9_9(133-150)Online publication date: 2-Jul-2024
https://doi.org/10.1007/978-3-031-64608-9_9
Emeric AVictor C(2024)Interpretable Cross-Platform Coordination Detection on Social NetworksComplex Networks & Their Applications XII10.1007/978-3-031-53503-1_12(143-155)Online publication date: 29-Feb-2024
https://doi.org/10.1007/978-3-031-53503-1_12
Lim DToriumi FYoshida MTanaka MYang K(2023)The variant of efforts avoiding strain: successful correction of a scientific discourse related to COVID-19Journal of Computational Social Science10.1007/s42001-023-00223-w7:1(1-21)Online publication date: 26-Oct-2023
https://doi.org/10.1007/s42001-023-00223-w
Ng KIamnitchi A(2023)Coordinated Information Campaigns on Social Media: A Multifaceted Framework for Detection and AnalysisDisinformation in Open Online Media10.1007/978-3-031-47896-3_8(103-118)Online publication date: 21-Nov-2023
https://dl.acm.org/doi/10.1007/978-3-031-47896-3_8
Reubold JEscher SPflugmacher JStrufe T(2022)Dissecting chirping patterns of invasive Tweeter flocks in the German Twitter forestOnline Social Networks and Media10.1016/j.osnem.2022.10022831(100228)Online publication date: Sep-2022
https://doi.org/10.1016/j.osnem.2022.100228
Weber DFalzon LMitchell LNasim M(2022)Promoting and countering misinformation during Australia’s 2019–2020 bushfires: a case study of polarisationSocial Network Analysis and Mining10.1007/s13278-022-00892-x12:1Online publication date: 24-Jun-2022
https://doi.org/10.1007/s13278-022-00892-x
Schellekens J(2021)Release the bots of war: social media and Artificial Intelligence as international cyber attackPrzegląd Europejski10.31338/1641-2478pe.4.21.10(163-179)Online publication date: 9-Dec-2021
https://doi.org/10.31338/1641-2478pe.4.21.10
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Login options

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

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents