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Do Events Change Opinions on Social Media? Studying the 2016 US Presidential Debates

  • Sopan Khosla
  • Niyati ChhayaEmail author
  • Shivam Jindal
  • Oindrila Saha
  • Milind Srivastava
Conference paper
  • 253 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11864)

Abstract

Social media is the primary platform for discussions and reactions during various social events. Studies in this space focus on the aggregate opinion and sentiment analysis but fail to analyze the micro-dynamics. In this work, we present a case study of the 2016 US Presidential Debates, analyzing the user opinion micro-dynamics across the timeline. We present an opinion variation analysis coupled with micro and macro level user analysis in order to explain opinion change. We also identify and characterize varied user-groups derived through this analyses. We discover that aggregate change in opinion is better explained by the differential influx of polarized population rather than the change in individual’s stance or opinion.

References

  1. 1.
    Agarwal, T., Burghardt, K., Lerman, K.: On quitting: performance and practice in online game play. In: Eleventh International AAAI Conference on Web and Social Media (2017)Google Scholar
  2. 2.
    Alipourfard, N., Fennell, P.G., Lerman, K.: Can you trust the trend? discovering Simpson’s paradoxes in social data. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 19–27. ACM (2018)Google Scholar
  3. 3.
    Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. J. Econ. Perspect. 31(2), 211–36 (2017)CrossRefGoogle Scholar
  4. 4.
    Anstead, N., O’Loughlin, B.: Social media analysis and public opinion: the 2010 UK general election. J. Comput.-Mediated Commun. 20(2), 204–220 (2014)CrossRefGoogle Scholar
  5. 5.
    Bickel, P.J., Hammel, E.A., O’Connell, J.W.: Sex bias in graduate admissions: data from Berkeley. Science 187(4175), 398–404 (1975)CrossRefGoogle Scholar
  6. 6.
    Borge-Holthoefer, J., Magdy, W., Darwish, K., Weber, I.: Content and network dynamics behind Egyptian political polarization on Twitter. In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, pp. 700–711. ACM (2015)Google Scholar
  7. 7.
    Bovet, A., Morone, F., Makse, H.A.: Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump. Sci. Rep. 8(1), 8673 (2018)CrossRefGoogle Scholar
  8. 8.
    Chen, Y.C., Liu, Z.Y., Kao, H.Y.: IKM at semeval-2017 task 8: convolutional neural networks for stance detection and rumor verification. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 465–469 (2017)Google Scholar
  9. 9.
    Himelboim, I., Sweetser, K.D., Tinkham, S.F., Cameron, K., Danelo, M., West, K.: Valence-based homophily on twitter: network analysis of emotions and political talk in the 2012 presidential election. New Med. Soc. 18(7), 1382–1400 (2016)CrossRefGoogle Scholar
  10. 10.
    Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751 (2014)Google Scholar
  11. 11.
    Lai, M., Hernández Farías, D.I., Patti, V., Rosso, P.: Friends and enemies of Clinton and Trump: using context for detecting stance in political Tweets. In: Sidorov, G., Herrera-Alcántara, O. (eds.) MICAI 2016. LNCS (LNAI), vol. 10061, pp. 155–168. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-62434-1_13CrossRefGoogle Scholar
  12. 12.
    Littman, J., Wrubel, L., Kerchner, D.: 2016 United States presidential election Tweet ids (2016).  https://doi.org/10.7910/DVN/PDI7IN
  13. 13.
    Liu, C., et al.: IUCL at semeval-2016 task 6: an ensemble model for stance detection in Twitter. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp. 394–400 (2016)Google Scholar
  14. 14.
    Martinez-Romo, J., Araujo, L., Borge-Holthoefer, J., Arenas, A., Capitán, J.A., Cuesta, J.A.: Disentangling categorical relationships through a graph of co-occurrences. Phys. Rev. E 84(4), 046108 (2011)CrossRefGoogle Scholar
  15. 15.
    Mohammad, S., Kiritchenko, S., Sobhani, P., Zhu, X., Cherry, C.: Semeval-2016 task 6: detecting stance in tweets. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp. 31–41 (2016)Google Scholar
  16. 16.
    Patra, B.G., Das, D., Bandyopadhyay, S.: JU\_NLP at semeval-2016 task 6: detecting stance in Tweets using support vector machines. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp. 440–444 (2016)Google Scholar
  17. 17.
    Pennebaker, J.W., Boyd, R.L., Jordan, K., Blackburn, K.: The development and psychometric properties of LIWC2015. Technical report (2015)Google Scholar
  18. 18.
    Primario, S., Borrelli, D., Iandoli, L., Zollo, G., Lipizzi, C.: Measuring polarization in Twitter enabled in online political conversation: the case of 2016 US presidential election. In: 2017 IEEE International Conference on Information Reuse and Integration (IRI), pp. 607–613. IEEE (2017)Google Scholar
  19. 19.
    Romero, D.M., Meeder, B., Kleinberg, J.: Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on Twitter. In: Proceedings of the 20th International Conference on World Wide Web, pp. 695–704. ACM (2011)Google Scholar
  20. 20.
    Wei, W., Zhang, X., Liu, X., Chen, W., Wang, T.: pkudblab at SemEVAL-2016 task 6: a specific convolutional neural network system for effective stance detection. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp. 384–388 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sopan Khosla
    • 1
  • Niyati Chhaya
    • 1
    Email author
  • Shivam Jindal
    • 2
  • Oindrila Saha
    • 3
  • Milind Srivastava
    • 4
  1. 1.Big Data Experience Lab, Adobe ResearchBangaloreIndia
  2. 2.Indian Institute of Technology, RoorkeeRoorkeeIndia
  3. 3.Indian Institute of Technology, KharagpurKharagpurIndia
  4. 4.Indian Institute of Technology, MadrasChennaiIndia

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