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Detecting Opinion Polarisation on Twitter by Constructing Pseudo-Bimodal Networks of Mentions and Retweets

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Book cover Information Retrieval (RuSSIR 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 573))

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Abstract

We present a novel approach to analyze and visualize opinion polarisation on Twitter based on graph features of communication networks extracted from tweets. We show that opinion polarisation can be legibly observed on unimodal projections of artificially created bimodal networks, where the most popular users in retweet and mention networks are considered nodes of the second mode. For this purpose, we select a subset of top users based on their PageRank values and assign them to be the second mode in our networks, thus called pseudo-bimodal. After projecting them onto the set of “bottom” users and vice versa, we get unimodal networks with more distinct clusters and visually coherent community separation. We developed our approach on a dataset gathered during the Russian protest meetings on 24th of December, 2011 and tested it on another dataset by Conover [13] used to analyze political polarisation, showing that our approach not only works well on our data but also improves the results from previous research on that phenomena.

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Notes

  1. 1.

    The term pseudo-bimodal networks is based on the previously introduced notion of pseudo-tricluster in two paired bimodal networks [16].

References

  1. Adamic, L.A., Glance, N.: The political blogosphere and the 2004 U.S. election: divided they blog. In: Proceedings of the 3rd International Workshop on Link Discovery, LinkKDD 2005, pp. 36–43. ACM, New York, NY, USA (2005). http://doi.acm.org/10.1145/1134271.1134277

  2. Aramaki, E., Maskawa, S., Morita, M.: Twitter catches the flu: detecting influenza epidemics using twitter. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1568–1576. Association for Computational Linguistics (2011)

    Google Scholar 

  3. Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining - WSDM 2011, p. 65. ACM Press, New York, New York, USA (2011). http://portal.acm.org/citation.cfm?doid=1935826.1935845

  4. Bennett, W.L., Johnson, C.N.: A model of crowd-enabled organization: theory and methods for understanding the role of Twitter in the occupy protests. Int. J. Commun. 8, 646–672 (2014)

    Google Scholar 

  5. Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech.: Theory Exp. 2008(10), P10008 (2008)

    Article  Google Scholar 

  6. Bollen, J., Mao, H., Zeng, X.J.: Twitter mood predicts the stock market (2010). arXiv preprint arxiv:1010.3003

  7. Bruns, A., Highfield, T., Burgess, J.: The Arab spring and social media audiences: English and Arabic Twitter users and their networks. Am. Behav. Sci. 57(7), 871–898 (2013). http://abs.sagepub.com/cgi/doi/10.1177/0002764213479374

    Article  Google Scholar 

  8. Bruns, A., Burgess, J.E.: The use of Twitter hashtags in the formation of ad hoc publics. In: 6th European Consortium for Political Research General Conference (2011)

    Google Scholar 

  9. Bruns, A., Highfield, T.: Political networks on Twitter. Inf. Commun. Soc. 16(5), 667–691 (2013). http://www.tandfonline.com/doi/abs/10.1080/1369118X.2013.782328

    Article  Google Scholar 

  10. Bulearca, M., Bulearca, S.: Twitter: a viable marketing tool for SMEs. Glob. Bus. Manag. Res.: Int. J. 2(4), 296–309 (2010)

    Google Scholar 

  11. Cha, M., Haddai, H., Benevenuto, F., Gummadi, K.P.: Measuring user influence in Twitter: the million follower fallacy. In: International AAAI Conference on Weblogs and Social Media (2010)

    Google Scholar 

  12. Comunello, F., Anzera, G.: Will the revolution be tweeted? A conceptual framework for understanding the social media and the Arab Spring. Islam Christ. Muslim Relat. 23(4), 453–470 (2012). http://www.tandfonline.com/doi/abs/10.1080/09596410.2012.712435

    Article  Google Scholar 

  13. Conover, M., Ratkiewicz, J., Francisco, M., Gonalves, B., Menczer, F., Flammini, A.: Political polarization on Twitter. In: ICWSM (2011). http://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/viewFile/2847

  14. Coppersmith, G., Dredze, M., Harman, C.: Quantifying mental health signals in Twitter. In: Proceedings of ACL Workshop on Computational Linguistics and Clinical Psychology, Association for Computational Linguistics (2014)

    Google Scholar 

  15. Gayo-Avello, D.: A meta-analysis of state-of-the-art electoral prediction from Twitter data. Soc. Sci. Comput. Rev. 31(6), 649–679 (2013). http://ssc.sagepub.com/cgi/doi/10.1177/0894439313493979

    Article  Google Scholar 

  16. Gnatyshak, D., Ignatov, D.I., Semenov, A., Poelmans, J.: Gaining insight in social networks with biclustering and triclustering. In: Aseeva, N., Babkin, E., Kozyrev, O. (eds.) BIR 2012. LNBIP, vol. 128, pp. 162–171. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  17. Golbeck, J., Hansen, D.: A method for computing political preference among Twitter followers. Soc. Netw. 36, 177–184 (2014). http://linkinghub.elsevier.com/retrieve/pii/S0378873313000683

    Article  Google Scholar 

  18. Gonzalez-Bailon, S., Borge-Holthoefer, J., Moreno, Y.: Broadcasters and hidden influentials in online protest diffusion. Am. Behav. Sci. 57(7), 943–965 (2013). http://abs.sagepub.com/cgi/doi/10.1177/0002764213479371

    Article  Google Scholar 

  19. González-Bailón, S., Borge-Holthoefer, J., Rivero, A., Moreno, Y.: The dynamics of protest recruitment through an online network. Sci. Rep. 1, 197 (2011). http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3240992&tool=pmcentrez&rendertype=abstract

  20. Highfield, T.: Mapping intermedia news flows: topical discussions in the Australian and French political blogospheres. Ph.D. thesis, Queensland University of Technology (2011)

    Google Scholar 

  21. Newman, M.E.J.: Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Phys. Rev. E 64(1), 016132 (2001)

    Article  Google Scholar 

  22. Nikiporets-Takigawa, G.: Tweeting the Russian protests. Digital Icons: Stud. Russ. Eurasioan Cent. Eur. New Media 9(2013), 1–25 (2013)

    Google Scholar 

  23. Page, L., Brin, S., Rajeev, M., Terry, W.: The pagerank citation ranking: bringing order to the web. Technical report, Stanford University (1998)

    Google Scholar 

  24. Vieweg, S., Hughes, A.L., Starbird, K., Palen, L.: Microblogging during two natural hazards events: what twitter may contribute to situational awareness. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1079–1088. ACM (2010)

    Google Scholar 

  25. Yardi, S., Boyd, D.: Dynamic debates: an analysis of group polarization over time on Twitter. Bull. Sci. Technol. Soc. 30(5), 316–327 (2010). http://bst.sagepub.com/cgi/doi/10.1177/0270467610380011

    Article  Google Scholar 

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Acknowledgements

This paper was prepared within the framework of a subsidy granted to HSE by the Government of Russian Federation for implementation of the Global Competitiveness Program. The work of Sergey Nikolenko was supported by the Russian Science Foundation grant no. 15-11-10019.

Author information

Authors and Affiliations

  1. International Laboratory for Applied Network Research, National Research University Higher School of Economics, Moscow, Russian Federation

    Igor Zakhlebin & Aleksandr Semenov

  2. Institute of Sociology, Russian Academy of Sciences, Moscow, Russia

    Alexander Tolmach

  3. Kazan (Volga Region) Federal University, Kazan, Russia

    Sergey Nikolenko

  4. Laboratory for Internet Studies, National Research University – Higher School of Economics, St. Petersburg, Russia

    Sergey Nikolenko

  5. Steklov Institute of Mathematics at St. Petersburg, St. Petersburg, Russia

    Sergey Nikolenko

Authors
  1. Igor Zakhlebin
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  2. Aleksandr Semenov
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  3. Alexander Tolmach
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  4. Sergey Nikolenko
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Corresponding author

Correspondence to Sergey Nikolenko .

Editor information

Editors and Affiliations

  1. Ural Federal University , Yekaterinburg, Russia

    Pavel Braslavski

  2. University of Amsterdam, Amsterdam, The Netherlands

    Ilya Markov

  3. University of Florida , Gainsville, Florida, USA

    Panos Pardalos

  4. Eurecat , Barcelona, Spain

    Yana Volkovich

  5. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  6. National Research University Higher School of Economics, Saint Petersburg, Russia

    Sergei Koltsov

  7. National Research University Higher School of Economics, Saint Petersburg, Russia

    Olessia Koltsova

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Zakhlebin, I., Semenov, A., Tolmach, A., Nikolenko, S. (2016). Detecting Opinion Polarisation on Twitter by Constructing Pseudo-Bimodal Networks of Mentions and Retweets. In: Braslavski, P., et al. Information Retrieval. RuSSIR 2015. Communications in Computer and Information Science, vol 573. Springer, Cham. https://doi.org/10.1007/978-3-319-41718-9_10

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  • DOI: https://doi.org/10.1007/978-3-319-41718-9_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41717-2

  • Online ISBN: 978-3-319-41718-9

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