The Untold Story of USA Presidential Elections in 2016 - Insights from Twitter Analytics

  • Purva GroverEmail author
  • Arpan Kumar Kar
  • Yogesh K. Dwivedi
  • Marijn Janssen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10595)


Elections are the most critical events for any nation and paves the path for future growth and prosperity of the economy. Due to its high impact, a lot of discussions take place among all stakeholders in social media. In this study, we attempt to examine the discussions surrounding USA Election, 2016 in Twitter. Further we highlight some of the domains influencing the voter behaviour by applying the outcome of Twitter analytics to Newman and Sheth’s model of Voter Choice. Through the analysis of 784,153 tweets from 287,838 users over 18 weeks, we present interesting findings on what may have affected the polarization of USA elections.


Social media Social media analytics Twitter analytics Information propagation Public policy 


  1. 1.
    Achrekar, H., Gandhe, A., Lazarus, R., Yu, S.H., Liu, B.: Predicting flu trends using twitter data. In: IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 702–707 (2011)Google Scholar
  2. 2.
    Adams, A., McCorkindale, T.: Dialogue and transparency: a content analysis of how the 2012 presidential candidates used twitter. Public Relat. Rev. 39(4), 357–359 (2013)CrossRefGoogle Scholar
  3. 3.
    Asur, S., Huberman, B.A..: Predicting the future with social media. In: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, pp. 492–499 (2010)Google Scholar
  4. 4.
    Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)CrossRefGoogle Scholar
  5. 5.
    Burnap, P., Gibson, R., Sloan, L., Southern, R., Williams, M.: 140 characters to victory? Using Twitter to predict the UK 2015 General Election. Electoral. Stud. 41, 230–233 (2016)CrossRefGoogle Scholar
  6. 6.
    Burnap, P., Rana, O.F., Avis, N., Williams, M., Housley, W., Edwards, A., Sloan, L.: Detecting tension in online communities with computational Twitter analysis. Technol. Forecast. Soc. Chang. 95, 96–108 (2015)CrossRefGoogle Scholar
  7. 7.
    Cha, M., Benevenuto, F., Haddadi, H., Gummadi, K.: The world of connections and information flow in Twitter. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 42(4), 991–998 (2012)CrossRefGoogle Scholar
  8. 8.
    Chae, B.K.: Insights from hashtag# supplychain and Twitter analytics: considering Twitter and Twitter data for supply chain practice and research. Int. J. Prod. Econ. 165, 247–259 (2015)CrossRefGoogle Scholar
  9. 9.
    Chatfield, A.T., Scholl, H.J.J., Brajawidagda, U.: Tsunami early warnings via Twitter in government: net-savvy citizens’ co-production of time-critical public information services. Gov. Inf. Q. 30(4), 377–386 (2013)CrossRefGoogle Scholar
  10. 10.
    Cody, E.M., Reagan, A.J., Mitchell, L., Dodds, P.S., Danforth, C.M.: Climate change sentiment on twitter: an unsolicited public opinion poll. PloS One 10(8), e0136092 (2015)CrossRefGoogle Scholar
  11. 11.
    Cohen, R., Ruths, D.: Classifying political orientation on Twitter: it’s not easy!. In: ICWSM (2013)Google Scholar
  12. 12.
    Ganis, M., Kohirkar, A.: Social Media Analytics: Techniques and Insights for Extracting Business Value Out of Social Media. IBM Press, New York (2015)Google Scholar
  13. 13.
    Hale, S.A.: Global connectivity and multilinguals in the Twitter network. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 833–842. ACM (2014)Google Scholar
  14. 14.
    Harris, J.K., Moreland-Russell, S., Choucair, B., Mansour, R., Staub, M., Simmons, K.: Tweeting for and against public health policy: response to the Chicago Department of Public Health’s electronic cigarette Twitter campaign. J. Med. Internet Res. 16(10) (2014)Google Scholar
  15. 15.
    Heller Baird, C., Parasnis, G.: From social media to social customer relationship management. Strategy Leadersh. 39(5), 30–37 (2011)CrossRefGoogle Scholar
  16. 16.
    Henderson, A., Bowley, R.: Authentic dialogue? The role of “friendship” in a social media recruitment campaign. J. Commun. Manage. 14(3), 237–257 (2010)CrossRefGoogle Scholar
  17. 17.
    HerdaĞdelen, A., Zuo, W., Gard-Murray, A., Bar-Yam, Y.: An exploration of social identity: the geography and politics of news-sharing communities in twitter. Complexity 19(2), 10–20 (2013)CrossRefGoogle Scholar
  18. 18.
    Ceron, A., Curini, L., Iacus, S.M., Porro, G.: Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France. New Media Soc. 16(2), 340–358 (2014)CrossRefGoogle Scholar
  19. 19.
    Joseph, N., Kar, A.K., Ilavarasan, V., Ganesh, S.: Review of discussions on Internet of Things (IoT): insights from Twitter analytics. Forthcoming J. Glob. Inf. Manage. 25(2), 38–51 (2016)CrossRefGoogle Scholar
  20. 20.
    Kim, A.J., Ko, E.: Do social media marketing activities enhance customer equity? An empirical study of luxury fashion brand. J. Bus. Res. 65(10), 1480–1486 (2012)CrossRefGoogle Scholar
  21. 21.
    Kolchyna, O., Souza, T.T., Treleaven, P., Aste, T.: Twitter sentiment analysis (2015)Google Scholar
  22. 22.
    Lakhiwal, A., Kar, A.K.: Insights from Twitter analytics: modeling social media personality dimensions and impact of breakthrough events. In: Dwivedi, Y.K., et al. (eds.) I3E 2016. LNCS, vol. 9844, pp. 533–544. Springer, Cham (2016). doi: 10.1007/978-3-319-45234-0_47 Google Scholar
  23. 23.
    Lampos, V., Aletras, N., Preoţiuc-Pietro, D., Cohn, T.: Predicting and characterising user impact on Twitter. In: 14th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2014, pp. 405–413 (2014)Google Scholar
  24. 24.
    Larsson, A.O., Moe, H.: Studying political microblogging: Twitter users in the 2010 Swedish election campaign. New Media Soc. 14(5), 729–747 (2012)CrossRefGoogle Scholar
  25. 25.
    Lau, J.H., Collier, N., Baldwin, T.: On-line trend analysis with topic models: #twitter trends detection topic model online. In: COLING, pp. 1519–1534 (2012)Google Scholar
  26. 26.
    Llewellyn, C., Grover, C., Alex, B., Oberlander, J., Tobin, R.: Extracting a topic specific dataset from a Twitter archive. In: Kapidakis, S., Mazurek, C., Werla, M. (eds.) TPDL 2015. LNCS, vol. 9316, pp. 364–367. Springer, Cham (2015). doi: 10.1007/978-3-319-24592-8_36 CrossRefGoogle Scholar
  27. 27.
    Mathioudakis, M., Koudas, N.: Twittermonitor: trend detection over the twitter stream. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, pp. 1155–1158 (2010)Google Scholar
  28. 28.
    Munar, A.M., Jacobsen, J.K.S.: Motivations for sharing tourism experiences through social media. Tour. Manag. 43, 46–54 (2014)CrossRefGoogle Scholar
  29. 29.
    Myslín, M., Zhu, S.H., Chapman, W., Conway, M.: Using Twitter to examine smoking behavior and perceptions of emerging tobacco products. J. Med. Internet Res. 15(8) (2013)Google Scholar
  30. 30.
    Neiger, B.L., Thackeray, R., Van Wagenen, S.A., Hanson, C.L., West, J.H., Barnes, M.D., Fagen, M.C.: Use of social media in health promotion purposes, key performance indicators, and evaluation metrics. Health Promot. Pract. 13(2), 159–164 (2012)CrossRefGoogle Scholar
  31. 31.
    Nooralahzadeh, F., Arunachalam, V., Chiru, C.G.: 2012 presidential elections on Twitter–an analysis of how the US and French election were reflected in tweets. In: 2013 19th International Conference on Control Systems and Computer Science, pp. 240–246. IEEE (2013)Google Scholar
  32. 32.
    Ou, G., Chen, W., Wang, T., Wei, Z., Li, B., Yang, D., Wong, K.F.: Exploiting community emotion for microblog event detection. In: EMNLP, pp. 1159–1168 (2014)Google Scholar
  33. 33.
    Purohit, H., Hampton, A., Shalin, V.L., Sheth, A.P., Flach, J., Bhatt, S.: What kind of #conversation is Twitter? Mining #psycholinguistic cues for emergency coordination. Comput. Hum. Behav. 29(6), 2438–2447 (2013)CrossRefGoogle Scholar
  34. 34.
    Reuben, R.: The use of social media in higher education for marketing and communications: a guide for professionals in higher education, 04-420 (2008)Google Scholar
  35. 35.
    Hassan, S.; Miriam, F.;Yulan, H., and Harith, A.: Evaluation datasets for Twitter sentiment analysis: a survey and a new dataset, the STS-Gold. In: 1st International Workshop on Emotion and Sentiment in Social and Expressive Media: Approaches and Perspectives from AI (ESSEM 2013), Turin, Italy (2013)Google Scholar
  36. 36.
    Shuai, X., Pepe, A., Bollen, J.: How the scientific community reacts to newly submitted preprints: article downloads, twitter mentions, and citations. PloS One 7(11), e47523 (2012)CrossRefGoogle Scholar
  37. 37.
    Singh, A.: Social media and corporate agility. Glob. J. Flex. Syst. Manage. 14(4), 255–260 (2013)CrossRefGoogle Scholar
  38. 38.
    Singh, V.K., Gao, M., Jain, R.: Situation detection and control using spatio-temporal analysis of microblogs. In: Proceedings of the 19th International Conference on World Wide Web, pp. 1181–1182. ACM (2010)Google Scholar
  39. 39.
    Tao, K., Hauff, C., Houben, G.J., Abel, F., Wachsmuth, G.: Facilitating Twitter data analytics: platform, language and functionality. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 421–430. IEEE (2014)Google Scholar
  40. 40.
    Thackeray, R., Neiger, B.L., Hanson, C.L., McKenzie, J.F.: Enhancing promotional strategies within social marketing programs: use of Web 2.0 social media. Health Promot. Pract. 9(4), 338–343 (2008)CrossRefGoogle Scholar
  41. 41.
    Utsuro, T., Zhao, C., Xu, L., Li, J., Kawada, Y.: An empirical analysis on comparing market share with concerns on companies measured through search engine suggests. Glob. J. Flex. Syst. Manage. 18, 3–19 (2016)CrossRefGoogle Scholar
  42. 42.
    Walther, M., Kaisser, M.: Geo-spatial event detection in the Twitter stream. In: Serdyukov, P., Braslavski, P., Kuznetsov, S.O., Kamps, J., Rüger, S., Agichtein, E., Segalovich, I., Yilmaz, E. (eds.) ECIR 2013. LNCS, vol. 7814, pp. 356–367. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-36973-5_30 CrossRefGoogle Scholar
  43. 43.
    Newman, B.I., Sheth, J.N.: A model of primary voter behavior. J. Consum. Res. 12(2), 178–187 (1985)CrossRefGoogle Scholar
  44. 44.
    Kassarjian, H.H.: Content analysis in consumer research. J. Consum. Res. 4(1), 8–18 (1977)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  • Purva Grover
    • 1
    Email author
  • Arpan Kumar Kar
    • 1
  • Yogesh K. Dwivedi
    • 2
  • Marijn Janssen
    • 3
  1. 1.DMSIndian Institute of TechnologyDelhiIndia
  2. 2.School of Business and EconomicsSwansea UniversitySwanseaUK
  3. 3.Delft University of TechnologyDelftNetherlands

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