Identifying Opinion Drivers on Social Media

  • Anish BhanushaliEmail author
  • Raksha Pavagada Subbanarasimha
  • Srinath Srinivasa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10574)


Social media is increasingly playing a central role in commercial and political strategies, making it an imperative to understand its dynamics. In our work, we propose a model of social media as a “marketplace of opinions.” Online social media is a participatory medium where several vested interests invest their opinions on disparate issues, and actively seek to establish a narrative that yields them positive returns from the population. This paper focuses on the problem of identifying such potential “drivers” of opinions for a given topic on social media. The intention to drive opinions are characterized by the following observable parameters: (a) significant level of proactive interest in the issue, and (b) narrow focus in terms of their distribution of topics. We test this hypothesis by building a computational model over Twitter data. Since we are trying to detect an intentional entity (intention to drive opinions), we resort to human judgment as the benchmark, against which we compare the algorithm. Opinion drivers are also shown to reflect the topical distribution of the trend better than users with high activity or impact. Identifying opinion drivers helps us reduce a trending topic to its “signature” comprising of the set of its opinion-drivers and the opinions driven by them.


Social media Drivers Opinion marketplace 


  1. 1.
    Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer: quantifying influence on Twitter. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM 2011, New York, NY, USA, pp. 65–74. ACM (2011)Google Scholar
  2. 2.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  3. 3.
    Borge-Holthoefer, J., Perra, N., Gonçalves, B., González-Bailón, S., Arenas, A., Moreno, Y., Vespignani, A.: The dynamics of information-driven coordination phenomena: a transfer entropy analysis. Sci. Adv. 2(4), e1501158 (2016)CrossRefGoogle Scholar
  4. 4.
    Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.P.: Measuring user influence in Twitter: the million follower fallacy. In: Proceedings of International AAAI Conference on Weblogs and Social Media, ICWSM 2010 (2010)Google Scholar
  5. 5.
    Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Measur. 20(1), 37–46 (1960)CrossRefGoogle Scholar
  6. 6.
    Ferrara, E., Varol, O., Menczer, F., Flammini, A.: Detection of promoted social media campaigns. In: Tenth International AAAI Conference on Web and Social Media (2016)Google Scholar
  7. 7.
    Ghosh, S., Sharma, N., Benevenuto, F., Ganguly, N., Gummadi, K.: Cognos: crowdsourcing search for topic experts in microblogs. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012, New York, NY, USA, pp. 575–590. ACM (2012)Google Scholar
  8. 8.
    Kendall, M.G.: A new measure of rank correlation. Biometrika 30(1–2), 81 (1938)CrossRefzbMATHGoogle Scholar
  9. 9.
    Lampos, V., Preoţiuc-Pietro, D., Cohn, T.: A user-centric model of voting intention from social media. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, ACL 2013, pp. 993–1003 (2013)Google Scholar
  10. 10.
    Lee, J.K., Choi, J., Kim, C., Kim, Y.: Social media, network heterogeneity, and opinion polarization. J. Commun. 64(4), 702–722 (2014)CrossRefGoogle Scholar
  11. 11.
    Lee, K., Caverlee, J., Cheng, Z., Sui, D.Z.: Content-driven detection of campaigns in social media. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM 2011, New York, NY, USA, pp. 551–556 (2011). ACMGoogle Scholar
  12. 12.
    Li, H., Mukherjee, A., Liu, B., Kornfield, R., Emery, S.: Detecting campaign promoters on twitter using markov random fields. In: Proceedings of the 2014 IEEE International Conference on Data Mining, ICDM 2014, Washington, DC, USA, pp. 290–299. IEEE Computer Society (2014)Google Scholar
  13. 13.
    Pal, A., Counts, S.: Identifying topical authorities in microblogs. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM 2011, New York, NY, USA, pp. 45–54. ACM (2011)Google Scholar
  14. 14.
    Teh, Y.W.: Dirichlet Process, pp. 280–287. Springer, Boston (2010)Google Scholar
  15. 15.
    Wagner, C., Liao, V., Pirolli, P., Nelson, L., Strohmaier, M.: It’s not in their tweets: modeling topical expertise of twitter users. In: Proceedings of the 2012 ASE/IEEE International Conference on Social Computing and 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust, SOCIALCOM-PASSAT 2012, Washington, DC, USA, pp. 91–100. IEEE Computer Society (2012)Google Scholar
  16. 16.
    Zhang, X., Zhu, S., Liang, W.: Detecting spam and promoting campaigns in the twitter social network. In: 2012 IEEE 12th International Conference on Data Mining, ICDM 2012, pp. 1194–1199 (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Anish Bhanushali
    • 1
    Email author
  • Raksha Pavagada Subbanarasimha
    • 1
  • Srinath Srinivasa
    • 1
  1. 1.International Institute of Information TechnologyBangaloreIndia

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