Skip to main content
SpringerLink
Log in

Forecasting Canadian Elections Using Twitter

  • Conference paper
  • First Online:
Advances in Artificial Intelligence (Canadian AI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9673))

Included in the following conference series:

Abstract

Experiments forecasting Canadian elections with Twitter are presented. A methodology for creating a representative Twitter sample is described and validated against census data. This sample and election polls are input into a VARX forecast model. The model covariance error monitors the forecast accuracy, measuring forecast confidence before the election occurs. The model is tested on several Canadian elections.

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

Access this chapter

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Institutional subscriptions

Notes

  1. 1.

    Microsoft’s Bing Maps, https://msdn.microsoft.com/en-us/library/ff701713.aspx.

References

  1. Achrekar, H., Gandhe, A., Lazarus, R., Yu, S.H., Liu, B.: Twitter improves seasonal influenza prediction. In: HEALTHINF, pp. 61–70 (2012)

    Google Scholar 

  2. Bermingham, A., Smeaton, A.F.: On using twitter to monitor political sentiment and predict election results. In: Sentiment Analysis where AI meets Psychology (SAAIP) Workshop at the International Joint Conference for Natural Language Processing (IJCNLP 2011), pp. 2–10 (2011)

    Google Scholar 

  3. Canada, S.: Toronto, ontario (code 3520005) and ontario (code 35) (table) (2012). http://www12.statcan.gc.ca/census-recensement/2011/dp-pd/prof/index.cfm?Lang=E

  4. Gayo Avello, D., Metaxas, P.T., Mustafaraj, E.: Limits of electoral predictions using twitter. In: Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media. Association for the Advancement of Artificial Intelligence (2011)

    Google Scholar 

  5. Gilbert, P.D.: Brief User’s Guide: Dynamic Systems Estimation (2006). http://cran.r-project.org/web/packages/dse/vignettes/Guide.pdf

  6. Gjoka, M., Kurant, M., Butts, C.T., Markopoulou, A.: Walking in facebook: a case study of unbiased sampling of osns. In: Proceedings of the IEEE INFOCOM, 2010, pp. 1–9. IEEE (2010)

    Google Scholar 

  7. Lumley, T.: Analysis of complex survey samples. J. Stat. Softw. 9(1), 1–19 (2004)

    Google Scholar 

  8. Metaxas, P.T., Mustafaraj, E., Gayo-Avello, D.: How (not) to predict elections. In: Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), pp. 165–171. IEEE (2011)

    Google Scholar 

  9. O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: linking text sentiment to public opinion time series. ICWSM 11, 122–129 (2010)

    Google Scholar 

  10. Propp, J.G., Wilson, D.B.: Exact sampling with coupled markov chains and applications to statistical mechanics. Random Struct. Algorithms 9(1–2), 223–252 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  11. Rinker, T.W.: qdap: Quantitative Discourse Analysis Package. University at Buffalo/SUNY, Buffalo (2013). http://github.com/trinker/qdap 2.2.4

    Google Scholar 

  12. Sang, E.T.K., Bos, J.: Predicting the 2011 dutch senate election results with twitter. In: Proceedings of the Workshop on Semantic Analysis in Social Media, pp. 53–60. Association for Computational Linguistics (2012)

    Google Scholar 

  13. Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with twitter: what 140 characters reveal about political sentiment. ICWSM 10, 178–185 (2010)

    Google Scholar 

  14. White, J.K., Li, G.: Method and system for sampling online social networks, April 2015. https://patents.google.com/patent/US20150095415A1/en

  15. White, K., Li, G., Japkowicz, N.: Sampling online social networks using coupling from the past. In: 2012 IEEE 12th International Conference on Data Mining Workshops (ICDMW), pp. 266–272. IEEE (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Advanced Symbolics, 41 York Street, 4th Floor, Ottawa, ON, Canada

    Kenton White

  2. School of Electrical Engineering and Computer Science, University of Ottawa, 800 King Edward Dr., Ottawa, ON, Canada

    Kenton White

Authors
  1. Kenton White
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kenton White .

Editor information

Editors and Affiliations

  1. Lakehead University, Thunder Bay, Ontario, Canada

    Richard Khoury

  2. National Research Council Canada , Ottawa, Canada

    Christopher Drummond

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

White, K. (2016). Forecasting Canadian Elections Using Twitter. In: Khoury, R., Drummond, C. (eds) Advances in Artificial Intelligence. Canadian AI 2016. Lecture Notes in Computer Science(), vol 9673. Springer, Cham. https://doi.org/10.1007/978-3-319-34111-8_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-34111-8_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-34110-1

  • Online ISBN: 978-3-319-34111-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation