Skip to main content
SpringerLink
Log in
Book cover

Designing Networks for Innovation and Improvisation pp 55–63Cite as

US Election Prediction: A Linguistic Analysis of US Twitter Users

  • Conference paper
  • First Online:

Part of the book series: Springer Proceedings in Complexity ((SPCOM))

Abstract

This chapter outlines a process to use linguistic data collected from Twitter to predict for whom a person will vote. The linguistic analysis makes use of previous research into profiling based on frequencies of words in natural language. We use data collected from social media to train several machine-learning algorithms in order to make predictions regarding a user’s voting preference in context of the ongoing US presidential election. This study is solely exploratory—we test the feasibility of election prediction based exclusively on natural language usage in tweets and thus will not include any other parameters in the prediction model. We present a methodology to achieve an accuracy of above 60 % in predicting a user’s voting preference using only the most basic linguistic features and discuss possible extensions and shortcomings.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    https://www.onlineprivacyfoundation.org/

References

  • Asur S, Huberman BA (2010) Predicting the future with social media. In: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol 1, IEEE Computer Society, Washington, pp 492–499

    Google Scholar 

  • Duggan M, Ellison NB, Lampe C, Lenhart A, Madden M (2015) Demographics of key social networking platforms. http://www.pewinternet.org/2015/01/09/demographics-of-key-social-networking-platforms-2/. Accessed 28 Feb 2016

  • Gayo-Avello D (2012) I wanted to predict elections with twitter and all I got was this Lousy Paper—A balanced survey on election prediction using twitter data. arXiv preprint arXiv:1204.6441

    Google Scholar 

  • Jungherr A, Jürgens P, Schoen H (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., & Welpe, I. M. “Predicting Elections with Twitter: What 140 characters reveal about political sentiment”. Social Sci Comput Rev 30(2):229–234

    Article  Google Scholar 

  • Kosinski M, Stillwell D, Graepel T (2013) Private traits and attributes are predictable from digital records of human behavior. Proc Natl Acad Sci U S A 110:5802–5805

    Article  Google Scholar 

  • Mondak JJ, Halperin KD (2008) A framework for the study of personality and political behaviour. Br J Polit Sci 38:335–362

    Article  Google Scholar 

  • Novak PK, Smailović J, Sluban B, Mozetič I (2015) Sentiment of emojis. PLoS One 10, e0144296

    Article  Google Scholar 

  • Pennebaker JW, Francis ME, Booth RJ (2001) Linguistic inquiry and word count: LIWC 2001. Lawrence Erlbaum Associates, Mahway, p 71

    Google Scholar 

  • Sumner C (2012) Personality prediction based on twitter stream. https://www.kaggle.com/c/twitter-personality-prediction. Accessed 1 Feb 2016

  • Sumner C, Byers A, Shearing M (2011) Determining personality traits & privacy concerns from Facebook activity. Black Hat Briefings 11:197–221

    Google Scholar 

  • Sumner C, Byers A, Boochever R, Park GJ (2012) Predicting dark triad personality traits from twitter usage and a linguistic analysis of tweets. In: Proceedings of the 2012 11th International Conference on Machine Learning and Applications (ICMLA), vol 2, IEEE Computer Society, Washington, pp 386–393

    Google Scholar 

  • Tumasjan A, Sprenger TO, Sandner PG, Welpe IM (2010) What 140 characters reveal about political sentiment. In: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, AAAI Press, Menlo Park

    Google Scholar 

  • Twitter Inc (2013) New Tweets per second record, and how!. https://blog.twitter.com/2013/new-tweets-per-second-record-and-how. Accessed 14 Feb 2016

  • Verhulst B, Eaves LJ, Hatemi PK (2012) Correlation not causation: The relationship between personality traits and political ideologies. Am J Polit Sci 56:34–51

    Article  Google Scholar 

  • Wald R, Khoshgoftaar T, Sumner C (2012a) Machine prediction of personality from Facebook profiles. In: Proceedings of the 2012 IEEE 13th International Conference on Information Reuse and Integration (IRI), IEEE Computer Society, Washington, pp 109–115

    Google Scholar 

  • Wald R, Khoshgoftaar TM, Napolitano A, Sumner C (2012b) Using twitter content to predict psychopathy. In: Proceedings of the 2012 11th International Conference on Machine Learning and Applications (ICMLA), vol 2, IEEE Computer Society, Washington, pp 394–401

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. MIT Center for Collective Intelligence, Cambridge, MA, USA

    Johannes Bachhuber

  2. University of Cologne, Cologne, Germany

    Christian Koppeel, Jeronim Morina & David Steinschulte

  3. Aalto University, Helsinki, Finland

    Kim Rejström

Authors
  1. Johannes Bachhuber
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christian Koppeel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jeronim Morina
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kim Rejström
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. David Steinschulte
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kim Rejström .

Editor information

Editors and Affiliations

  1. Department of Information Systems & Social Networks, University of Bamberg, Bamberg, Germany

    Matthäus P. Zylka

  2. Department of Information Systems & Social Networks, University of Bamberg, Bamberg, Germany

    Hauke Fuehres

  3. Department of Enterprise Engineering, Tor Vergata University of Rome, Rome, Italy

    Andrea Fronzetti Colladon

  4. MIT Center for Collective Intelligence, Cambridge, Massachusetts, USA

    Peter A. Gloor

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Bachhuber, J., Koppeel, C., Morina, J., Rejström, K., Steinschulte, D. (2016). US Election Prediction: A Linguistic Analysis of US Twitter Users. In: Zylka, M., Fuehres, H., Fronzetti Colladon, A., Gloor, P. (eds) Designing Networks for Innovation and Improvisation. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-42697-6_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-42697-6_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42696-9

  • Online ISBN: 978-3-319-42697-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation