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.
- Hunt Allcott and Matthew Gentzkow. 2017. Social media and fake news in the 2016 election. Technical Report. National Bureau of Economic Research.Google Scholar
- Sophie Chou and Deb Roy. 2017. Nasty, Brutish, and Short: What Makes Election News Popular on Twitter. In ICWSM. 492--495.Google Scholar
- 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 ScholarDigital Library
- Rachel Ehrenberg. 2012. Social media sway: Worries over political misinformation on Twitter attract scientists' attention. Science News 182, 8 (2012), 22--25.Google ScholarCross Ref
- Emilio Ferrara. 2017. Disinformation and social bot operations in the run up to the 2017 French presidential election. First Monday 22, 8 (2017).Google Scholar
- Emilio Ferrara. 2017. Disinformation and social bot operations in the run up to the 2017 French presidential election. (2017).Google Scholar
- 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 ScholarDigital Library
- 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 Scholar
- Patrick S Forscher and Nour S Kteily. 2017. A Psychological Profile of the AltRight. PsyArXiv (10 2017).Google Scholar
- 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 Scholar
- 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 Scholar
- 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 Scholar
- 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 ScholarDigital Library
- Ema Kuen and Mark Strembeck. 2017. An Analysis of the Twitter Discussion on the 2016 Austrian Presidential Elections. arXiv:1707.09939 (2017).Google Scholar
- Renaud Lambiotte, J-C Delvenne, and Mauricio Barahona. 2008. Laplacian dynamics and multiscale modular structure in networks. arXiv preprint arXiv:0812.1770 (2008).Google Scholar
- 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 ScholarDigital Library
- Clay Ramsay, Steven Kull, Evan Lewis, Stefan Subias, et al. 2010. Misinformation and the 2010 election: A study of the US electorate. (2010).Google Scholar
- 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 ScholarDigital Library
- 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 Scholar
- 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 Scholar
- Greg Ver Steeg. 2017. Unsupervised Learning via Total Correlation Explanation. arXiv preprint arXiv:1706.08984 (2017).Google Scholar
- 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 Scholar
- 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 ScholarDigital Library
- Liang Wu and Huan Liu. 2018. Tracing Fake-News Footprints: Characterizing Social Media Messages by How They Propagate. (2018).Google Scholar
- 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 Scholar
Index Terms
- From Alt-Right to Alt-Rechts: Twitter Analysis of the 2017 German Federal Election
Recommendations
Measuring Extremism: Validating an Alt-Right Twitter Accounts Dataset
Intelligent Data Engineering and Automated Learning – IDEAL 2018AbstractTwitter 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 NetworksAbstractSocial 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 MiningBlogging 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