Skip to main content
SpringerLink
Log in
Book cover

International Conference on Information

iConference 2019: Information in Contemporary Society pp 456–465Cite as

Political Popularity Analysis in Social Media

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11420))

Abstract

Popularity is a critical success factor for a politician and her/his party to win in elections and implement their plans. Finding the reasons behind the popularity can provide a stable political movement. This research attempts to measure popularity in Twitter using a mixed method. In recent years, Twitter data has provided an excellent opportunity for exploring public opinions by analyzing a large number of tweets. This study has collected and examined 4.5 million tweets related to a US politician, Senator Bernie Sanders. This study investigated eight economic reasons behind the senator’s popularity in Twitter. This research has benefits for politicians, informatics experts, and policymakers to explore public opinion. The collected data will also be available for further investigation.

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    https://github.com/amir-karami/Sanders-Tweets-Data.

References

  1. Bellin, E.: Reconsidering the robustness of authoritarianism in the middle east: lessons from the arab spring. Comp. Politics 44(2), 127–149 (2012)

    Article  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), p. 2 (2011)

    Google Scholar 

  3. Blei, D.M.: Probabilistic topic models. Commun. ACM 55(4), 77–84 (2012)

    Article  Google Scholar 

  4. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  5. Bonilla, Y., Rosa, J.: # Ferguson: digital protest, hashtag ethnography, and the racial politics of social media in the united states. Am. Ethnol. 42(1), 4–17 (2015)

    Article  Google Scholar 

  6. Boutet, A., Kim, H., Yoneki, E.: What’s in your tweets? I know who you supported in the UK 2010 general election. In: The International AAAI Conference on Weblogs and Social Media (ICWSM) (2012)

    Google Scholar 

  7. Carroll, J.: Economy, Terrorism Top Issues in 2004 Election Vote (2003). http://www.gallup.com/poll/9337/economy-terrorism-top-issues-2004-election-vote.aspx

  8. Collins, M., Karami, A.: Social media analysis for organizations: Us northeastern public and state libraries case study. In: Proceedings of the Southern Association for Information Systems (2018)

    Google Scholar 

  9. Cowling, D.: How political polling shapes public opinion (2015). http://www.bbc.com/news/uk-31504146

  10. Desilver, D.: 5 Facts About the Minimum Wage. Pew Research Center (2016). http://www.pewresearch.org/fact-tank/2017/01/04/5-facts-about-the-minimum-wage/

  11. Edwards-Levy, A.: Raising the Minimum Wage Is A Really, Really Popular Idea. The Huffington Post (2017). http://www.huffingtonpost.com/entry/minimum-wage-poll_us_570ead92e4b08a2d32b8e671

  12. Gaurav, M., Srivastava, A., Kumar, A., Miller, S.: Leveraging candidate popularity on twitter to predict election outcome. In: Proceedings of the 7th Workshop on Social Network Mining and Analysis, p. 7. ACM (2013)

    Google Scholar 

  13. Golder, S.A., Macy, M.W.: Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333(6051), 1878–1881 (2011)

    Article  Google Scholar 

  14. Gottfried, J., Shearer, E.: News Use Across Social Media Platforms 2016 (2016). http://www.journalism.org/2016/05/26/news-use-across-social-media-platforms-2016/

  15. He, X., Karami, A., Deng, C.: Examining the effects of online social relations on product ratings and adoption: evidence from an online social networking and rating site. Int. J. Web Based Communit. 13(3), 344–363 (2017)

    Article  Google Scholar 

  16. Hong, S.: Who benefits from twitter? Social media and political competition in the us house of representatives. Gov. Inf. Q. 30(4), 464–472 (2013)

    Article  Google Scholar 

  17. Hong, S., Nadler, D.: Which candidates do the public discuss online in an election campaign? The use of social media by 2012 presidential candidates and its impact on candidate salience. Gov. Inf. Q. 29(4), 455–461 (2012)

    Article  Google Scholar 

  18. Karami, A.: Fuzzy topic modeling for medical corpora. University of Maryland, Baltimore County (2015)

    Google Scholar 

  19. Karami, A.: Taming wild high dimensional text data with a fuzzy lash. In: IEEE International Conference on Data Mining Workshops (ICDMW), pp. 518–522. IEEE (2017)

    Google Scholar 

  20. Karami, A., Bennett, L.S., He, X.: Mining public opinion about economic issues: twitter and the us presidential election. Int. J. Strateg. Decis. Sci. (IJSDS) 9(1), 18–28 (2018)

    Article  Google Scholar 

  21. Karami, A., Collins, M.: What do the us west coast public libraries post on twitter? Proc. Assoc. Inf. Sci. Technol. 55(1), 216–225 (2018)

    Article  Google Scholar 

  22. Karami, A., Dahl, A.A., Turner-McGrievy, G., Kharrazi, H., Shaw, G.: Characterizing diabetes, diet, exercise, and obesity comments on twitter. Int. J. Inf. Manag. 38(1), 1–6 (2018)

    Article  Google Scholar 

  23. Karami, A., Gangopadhyay, A.: Fftm: a fuzzy feature transformation method for medical documents. Proc. BioNLP 2014, 128–133 (2014)

    Google Scholar 

  24. Karami, A., Gangopadhyay, A., Zhou, B., Karrazi, H.: Flatm: a fuzzy logic approach topic model for medical documents. In: Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC), 2015 Annual Conference of the North American, pp. 1–6. IEEE (2015)

    Google Scholar 

  25. Karami, A., Gangopadhyay, A., Zhou, B., Kharrazi, H.: A fuzzy approach model for uncovering hidden latent semantic structure in medical text collections. In: iConference 2015 Proceedings (2015)

    Google Scholar 

  26. Karami, A., Gangopadhyay, A., Zhou, B., Kharrazi, H.: Fuzzy approach topic discovery in health and medical corpora. Int. J. Fuzzy Syst. 20(4), 1334–1345 (2018)

    Article  Google Scholar 

  27. Karami, A., Pendergraft, N.M.: Computational analysis of insurance complaints: GEICO case study. In: International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation (2018)

    Google Scholar 

  28. Karami, A., Webb, F., Kitzie, V.L.: Characterizing transgender health issues in twitter. Proc. Assoc. Inf. Sci. Technol. 55(1), 207–215 (2018)

    Article  Google Scholar 

  29. Karami, A., Zhou, B.: Online review spam detection by new linguistic features. In: iConference 2015 Proceedings (2015)

    Google Scholar 

  30. Karami, A., Zhou, L.: Exploiting latent content based features for the detection of static sms spams. Proc. Am. Soc. Inf. Sci. Technol. 51(1), 1–4 (2014)

    Article  Google Scholar 

  31. Karami, A., Zhou, L.: Improving static sms spam detection by using new content-based features. In: The 20th Americas Conference on Information Systems (AMCIS) (2014)

    Google Scholar 

  32. Kitzie, V.L., Mohammadi, E., Karami, A.: “Life never matters in the democrats mind”: examining strategies of retweeted social bots during a mass shooting event. Proc. Assoc. Inf. Sci. Technol. 55(1), 254–263 (2018)

    Article  Google Scholar 

  33. Kushin, M.J., Yamamoto, M.: Did social media really matter? College students’ use of online media and political decision making in the 2008 election. Mass Commun. Soc. 13(5), 608–630 (2010)

    Article  Google Scholar 

  34. LaMarre, H.L., Suzuki-Lambrecht, Y.: Tweeting democracy? Examining twitter as an online public relations strategy for congressional campaigns. Public Relat. Rev. 39(4), 360–368 (2013)

    Article  Google Scholar 

  35. Lu, Y., Mei, Q., Zhai, C.: Investigating task performance of probabilistic topic models: an empirical study of PLSA and LDA. Inf. Retr. 14(2), 178–203 (2011)

    Article  Google Scholar 

  36. McCallum, A.K.: MALLET: A Machine Learning for Language Toolkit (2002). http://mallet.cs.umass.edu/topics.php

  37. Najafabadi, M.M., Domanski, R.J.: Hacktivism and distributed hashtag spoiling on twitter: tales of the# irantalks. First Monday 23(4) (2018)

    Google Scholar 

  38. Newport, F.: Majority in US Support Idea of Fed-funded Healthcare System. Gallup (2016). http://www.gallup.com/poll/191504/majority-support-idea-fed-funded-healthcare-system.aspx

  39. Pennebaker, J.W., Boyd, R.L., Jordan, K., Blackburn, K.: The Development and Psychometric Properties of LIWC2015. UT Faculty/Researcher Works (2015)

    Google Scholar 

  40. Pew Research Center: Top Voting Issues in 2016 Election (2016). http://www.people-press.org/2016/07/07/4-top-voting-issues-in-2016-election/

  41. Polling Report: International trade/global economy. NBC News/Wall Street J. (2016). http://www.pollingreport.com/trade.htm

  42. Pounds, S.: Is College Worth It? Americans See it as a Good Investment, Bankrate Survey Finds. Bankrate (2016). http://www.bankrate.com/finance/consumer-index/money-pulse-0816.aspx

  43. Rumer, A.: President Trump’s Twitter Habit is Leading other Politicians to Pick Up Their Smartphones. Time (2017). http://time.com/4822054/donald-trump-twitter-social-media-politicians/

  44. Saad, L.: Iraq and the Economy Are Top Issues to Voters (2008). http://www.gallup.com/poll/104320/iraq-economy-top-issues-voters.aspx

  45. Saad, L.: Economy Is Dominant Issue for Americans as Election Nears (2012). http://www.gallup.com/poll/158267/economy-dominant-issue-americans-election-nears.aspx

  46. Sander Website: Broad public support for bernies plan to expand social security. NBC News/Wall Street J. (2015). https://berniesanders.com/broad-public-support-for-bernies-plan-to-expand-social-security/

  47. 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 

  48. Sewell, R.R.: Who is following us? Data mining a library’s twitter followers. Libr. Hi Tech. 31(1), 160–170 (2013)

    Article  Google Scholar 

  49. Shaw Jr., G., Karami, A.: Computational content analysis of negative tweets for obesity, diet, diabetes, and exercise. Proc. Assoc. Inf. Sci. Technol. 54(1), 357–365 (2017)

    Article  Google Scholar 

  50. Shaw Jr., G., Karami, A.: An exploratory study of (#)exercise in the twittersphere. In: iConference 2019 Proceedings (2019)

    Google Scholar 

  51. Smit, K.: Marketing: 96 Amazing Social Media Statistics and Facts (2016). https://www.brandwatch.com/2016/03/96-amazing-social-media-statistics-and-facts-for-2016

  52. Stein, J.: Why Snapchat’s Snappy. Time (2017)

    Google Scholar 

  53. The Economist: Politics and Twitter: Sweet to Tweet (2010). http://www.economist.com/node/16056612

  54. The Huffington Post Pollster: Bernie Sanders Favorable Rating (2017). http://elections.huffingtonpost.com/pollster/bernie-sanders-favorable-rating

  55. Thomas, Z.: US Election 2016: Who’s Funding Trump, Sanders and the Rest? (2016). http://www.bbc.com/news/election-us-2016-35713168

  56. Tufekci, Z., Wilson, C.: Social media and the decision to participate in political protest: observations from tahrir square. J. Commun. 62(2), 363–379 (2012)

    Article  Google Scholar 

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

    Google Scholar 

  58. Valenzuela, S.: Unpacking the use of social media for protest behavior: the roles of information, opinion expression, and activism. Am. Behav. Sci. 57(7), 920–942 (2013)

    Article  Google Scholar 

  59. Valenzuela, S., Arriagada, A., Scherman, A.: The social media basis of youth protest behavior: the case of chile. J. Commun. 62(2), 299–314 (2012)

    Article  Google Scholar 

  60. Velencia, J.: Hillary Clinton’s 2016 Announcement Caused Twitter To Freak Out (2015). http://www.huffingtonpost.com/2015/04/13/hillary-clinton-announcement-on-social-media_n_7057020.html

  61. Wallach, H.M., Murray, I., Salakhutdinov, R., Mimno, D.: Evaluation methods for topic models. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 1105–1112. ACM (2009)

    Google Scholar 

  62. Webb, F., Karami, A., Kitzie, V.: Characterizing diseases and disorders in gay users’ tweets. In: Proceedings of the Southern Association for Information Systems (2018)

    Google Scholar 

Download references

Acknowledgements

This research is supported in part by the South Carolina Alliance for Minority Participation and the Science Undergraduate Research Fellowships and Exploration Scholars Program programs at the University of South Carolina. All opinions, findings, conclusions and recommendations in this paper are those of the authors and do not necessarily reflect the views of the funding agency.

Author information

Authors and Affiliations

  1. University of South Carolina, Columbia, SC, 29208, USA

    Amir Karami & Aida Elkouri

Authors
  1. Amir Karami
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aida Elkouri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amir Karami .

Editor information

Editors and Affiliations

  1. University of South Florida, Tampa, FL, USA

    Natalie Greene Taylor

  2. University of Maryland, College Park, MD, USA

    Caitlin Christian-Lamb

  3. University of Washington, Seattle, WA, USA

    Michelle H. Martin

  4. University of California, Irvine, Irvine, CA, USA

    Bonnie Nardi

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Karami, A., Elkouri, A. (2019). Political Popularity Analysis in Social Media. In: Taylor, N., Christian-Lamb, C., Martin, M., Nardi, B. (eds) Information in Contemporary Society. iConference 2019. Lecture Notes in Computer Science(), vol 11420. Springer, Cham. https://doi.org/10.1007/978-3-030-15742-5_44

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-15742-5_44

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-15741-8

  • Online ISBN: 978-3-030-15742-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation