Skip to main content

Event Analysis on the 2016 U.S. Presidential Election Using Social Media

Part of the Lecture Notes in Computer Science book series (LNISA,volume 10539)

Abstract

It is not surprising that social media have played an important role in shaping the political debate during the 2016 presidential election. The dynamics of social media provide a unique opportunity to detect and interpret the pivotal events and scandals of the candidates quantitatively. This paper examines several text-based analysis to determine which topics have a lasting impact on the election for the two main candidates, Clinton and Trump. About 135.5 million tweets are collected over the six weeks prior to the election. From these tweets, topic clustering, keyword extraction, and tweeter analysis are performed to better understand the impact of the events occurred during this period. Our analysis builds upon a social science foundation to provide another avenue for scholars to use in discerning how events detected from social media show the impacts of campaigns as well as campaign the election.

Keywords

  • Presidential election
  • Topic clustering
  • Keyword extraction
  • Twitter analysis
  • Social media

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-67217-5_13
  • Chapter length: 17 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   39.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-67217-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   54.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.

Notes

  1. 1.

    Twitter Streaming API: https://dev.twitter.com/streaming/overview.

  2. 2.

    New York Times API: https://developer.nytimes.com/archive_api.json.

  3. 3.

    See sielsen’s ratings: http://www.nielsen.com/us/en/insights/news/2016/first-presidential-debate-of-2016-draws-84-million-viewers.html.

References

  1. Shaw, D.R.: A study of presidential campaign event effects from 1952 to 1992. J. Polit. 61(2), 387–422 (1999)

    CrossRef  Google Scholar 

  2. Campbell, J.E., Norpoth, H., Abramowitz, A.I., Lewis-Beck, M.S., Tien, C., Erikson, R.S., Wlezien, C., Lockerbie, B., Holbrook, T.M., Jerôme, B., Jerôme-Speziari, V., Graefe, A., Armstrong, J.S., Jones, R.J., Cuzán, A.G.: Recap of the 2016 election forecasts. PS: Polit. Sci. Polit. 50(2), 331–338 (2017)

    Google Scholar 

  3. Lewis-Beck, M.S., Stegmaier, M.: US presidential election forecasting. PS: Polit. Sci. Polit. 47(2), 284–288 (2014)

    Google Scholar 

  4. Clark, T.S., Staton, J.K., Wang, Y., Agichtein, E.: Using Twitter to study public discourse in the wake of judicial decisions: public reactions to the supreme court’s same-sex marriage cases (2014)

    Google Scholar 

  5. Leigh, A., Wolfers, J.: Competing approaches to forecasting elections: economic models, opinion polling and prediction markets. Econ. Rec. 82(258), 325–340 (2006)

    CrossRef  Google Scholar 

  6. Abramowitz, A.I.: An improved model for predicting presidential election outcomes. PS: Polit. Sci. Polit. 21(4), 843–847 (1988)

    Google Scholar 

  7. Campbell, J.E., Wink, K.A.: Trial-heat forecasts of the presidential vote. Am. Polit. Q. 18(3), 251–269 (1990)

    CrossRef  Google Scholar 

  8. Campbell, J.E., Cherry, L.L., Wink, K.A.: The convention bump. Am. Polit. Q. 20(3), 287–307 (1992)

    CrossRef  Google Scholar 

  9. Norpoth, H., Bednarczuk, M.: History and primary: the Obama re-election. In: APSA 2012 Annual Meeting Paper, September 2012

    Google Scholar 

  10. Abramowitz, A.: Forecasting in a polarized era: the time for change model and the 2012 presidential election. PS: Polit. Sci. Polit. 45(4), 618–619 (2012)

    Google Scholar 

  11. Hillygus, D.S.: The evolution of election polling in the united states. Public Opin. Q. 75(5), 962–981 (2011)

    CrossRef  Google Scholar 

  12. Silver, N.: Who will win the presidency? (2016)

    Google Scholar 

  13. Gollin, A.E.: Polling and the news media. Public Opin. Q. 51(part 2: Supplement: 50th Anniversary Issue), S86 (1987)

    Google Scholar 

  14. Tenpas, K.D., McCann, J.A.: Testing the permanence of the permanent campaign: an analysis of presidential polling expenditures, 1977–2002. Public Opin. Q. 71(3), 349–366 (2007)

    CrossRef  Google Scholar 

  15. Iyengar, S., Norpoth, H., Hahn, K.S.: Consumer demand for election news: the horserace sells. J. Polit. 66(1), 157–175 (2004)

    CrossRef  Google Scholar 

  16. Rosenstiel, T.: Political polling and the new media culture: a case of more being less. Public Opin. Q. 69(5), 698–715 (2005)

    CrossRef  Google Scholar 

  17. Miller, P.R., Conover, P.J.: Red and blue states of mind. Polit. Res. Q. 68(2), 225–239 (2015)

    CrossRef  Google Scholar 

  18. Harris, L.: Election polling and research. Public Opin. Q. 21(1, Anniversary Issue Devoted to Twenty Years of Public Opinion Research), 108 (1957)

    Google Scholar 

  19. Jacobs, L.R., Shapiro, R.Y.: Issues, candidate image, and priming: the use of private polls in Kennedy’s 1960 presidential campaign. Am. Polit. Sci. Rev. 88(3), 527–540 (1994)

    CrossRef  Google Scholar 

  20. King, R., Schnitzer, M.: Contemporary use of private political polling. Public Opin. Q. 32(3), 431 (1968)

    CrossRef  Google Scholar 

  21. Jacobs, L.R.: Polling politics, media, and election campaigns. Public Opin. Q. 69(5), 635–641 (2005)

    CrossRef  Google Scholar 

  22. Jacobs, L.R., Shapiro, R.Y.: The rise of presidential polling: the nixon white house in historical perspective. Public Opin. Q. 59(2), 163 (1995)

    CrossRef  Google Scholar 

  23. Gelman, A., King, G.: Why are American presidential election campaign polls so variable when votes are so predictable? Br. J. Polit. Sci. 23(4), 409 (1993)

    CrossRef  Google Scholar 

  24. Arceneaux, K.: Do campaigns help voters learn? A cross-national analysis. Br. J. Polit. Sci. 36(1), 159 (2005)

    CrossRef  Google Scholar 

  25. Wlezien, C., Erikson, R.S.: The timeline of presidential election campaigns. J. Polit. 64(4), 969–993 (2002)

    CrossRef  Google Scholar 

  26. Atefeh, F., Khreich, W.: A survey of techniques for event detection in Twitter. Comput. Intell. 31(1), 132–164 (2013)

    CrossRef  MathSciNet  Google Scholar 

  27. Ivan, C., Moldovan, A.: Twitrends: a real time trending topics detection system for Twitter social network. Int. J. Comput. Appl. 152(4), 16–25 (2016)

    Google Scholar 

  28. Becker, H., Naaman, M., Gravano, L.: Beyond trending topics: real-world event identification on Twitter. In: ICWSM, vol. 11, pp. 438–441 (2011)

    Google Scholar 

  29. Yang, Y., Pierce, T., Carbonell, J.: A study of retrospective and on-line event detection. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR 1998. ACM Press (1998)

    Google Scholar 

  30. Conroy, J.M., O’leary, D.P.: Text summarization via hidden Markov models. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR 2001. ACM Press (2001)

    Google Scholar 

  31. Becker, H., Naaman, M., Gravano, L.: Selecting quality Twitter content for events. In: ICWSM 2011 (2011)

    Google Scholar 

  32. Yajuan, D., Zhimin, C., Furu, W., Ming, Z., Shum, H.Y.: Twitter topic summarization by ranking tweets using social influence and content quality. In: Proceedings of the 24th International Conference on Computational Linguistics, pp. 763–780 (2012)

    Google Scholar 

  33. Nenkova, A., McKeown, K.: A survey of text summarization techniques. In: Aggarwal, C., Zhai, C. (eds.) Mining Text Data, pp. 43–76. Springer, Boston (2012). doi:10.1007/978-1-4614-3223-4_3

    CrossRef  Google Scholar 

  34. Xu, W., Grishman, R., Meyers, A., Ritter, A.: A preliminary study of tweet summarization using information extraction. In: NAACL 2013, p. 20 (2013)

    Google Scholar 

  35. Matsuo, Y., Ishizuka, M.: Keyword extraction from a single document using word co-occurrence statistical information. Int. J. Artif. Intell. Tools 13(1), 157–169 (2004)

    CrossRef  Google Scholar 

  36. Mishra, A., Vishwakarma, S.: Analysis of TF-IDF model and its variant for document retrieval. In: 2015 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, December 2015

    Google Scholar 

  37. Ramos, J.: Using TF-IDF to determine word relevance in document queries. In: Proceedings of the First Instructional Conference on Machine Learning (2003)

    Google Scholar 

  38. Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606 (2016)

  39. Campr, M., Ježek, K.: Comparing semantic models for evaluating automatic document summarization. In: Král, P., Matoušek, V. (eds.) TSD 2015. LNCS, vol. 9302, pp. 252–260. Springer, Cham (2015). doi:10.1007/978-3-319-24033-6_29

    CrossRef  Google Scholar 

  40. Polettini, N.: The vector space model in information retrieval-term weighting problem. Entropy 1–9 (2004)

    Google Scholar 

  41. Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035. Society for Industrial and Applied Mathematics (2007)

    Google Scholar 

  42. Allcott, H.: Social Media and Fake News in the 2016 Election (2017)

    Google Scholar 

  43. Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals. In: Soviet Physics Doklady, vol. 10 (1966)

    Google Scholar 

  44. Damerau, F.J.: A technique for computer detection and correction of spelling errors. Commun. ACM 7(3), 171–176 (1964)

    CrossRef  Google Scholar 

  45. Bermingham, A., Smeaton, A.F.: On using Twitter to Monitor Political Sentiment and Predict Election Results (2011)

    Google Scholar 

  46. O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: linking text sentiment to public opinion time series. In: ICWSM 2011, pp. 122–129 (2010)

    Google Scholar 

  47. Marchetti-Bowick, M., Chambers, N.: Learning for microblogs with distant supervision: political forecasting with Twitter. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pp. 603–612. Association for Computational Linguistics (2012)

    Google Scholar 

  48. Viz 2016 (2015)

    Google Scholar 

  49. Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with twitter: what 140 characters reveal about political sentiment. ICWSM 10(1), 178–185 (2010)

    Google Scholar 

  50. Shin, B., Lee, T., Choi, J.D.: Lexicon integrated CNN models with attention for sentiment analysis. In: Proceedings of the EMNLP Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2017 (2017)

    Google Scholar 

  51. Novak, P.K., Smailović, J., Sluban, B., Mozetič, I.: Sentiment of emojis. PLoS ONE 10(12), e0144296 (2015)

    CrossRef  Google Scholar 

  52. Kilgarriff, A., Fellbaum, C.: WordNet: An Electronic Lexical Database, vol. 76. JSTOR, September 2000

    Google Scholar 

  53. Xiang, Y., Sarvary, M.: News consumption and media bias. Mark. Sci. 26(5), 611–628 (2007)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tarrek A. Shaban .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Shaban, T.A., Hexter, L., Choi, J.D. (2017). Event Analysis on the 2016 U.S. Presidential Election Using Social Media. In: Ciampaglia, G., Mashhadi, A., Yasseri, T. (eds) Social Informatics. SocInfo 2017. Lecture Notes in Computer Science(), vol 10539. Springer, Cham. https://doi.org/10.1007/978-3-319-67217-5_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67217-5_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67216-8

  • Online ISBN: 978-3-319-67217-5

  • eBook Packages: Computer ScienceComputer Science (R0)