Combining Network and Language Indicators for Tracking Conflict Intensity

  • Anna Rumshisky
  • Mikhail Gronas
  • Peter Potash
  • Mikhail Dubov
  • Alexey Romanov
  • Saurabh Kulshreshtha
  • Alex Gribov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10540)

Abstract

This work seeks to analyze the dynamics of social or political conflict as it develops over time, using a combination of network-based and language-based measures of conflict intensity derived from social media data. Specifically, we look at the random-walk based measure of graph polarization, text-based sentiment analysis, and the corresponding shift in word meaning and use by the opposing sides. We analyze the interplay of these views of conflict using the Ukraine-Russian Maidan crisis as a case study.

References

  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Collins, R.: C-escalation and D-escalation: a theory of the time-dynamics of conflict. Am. Sociol. Rev. 77(1), 1–20 (2012)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Deutsch, M., Coleman, P.T., Marcus, E.C.: The Handbook of Conflict Resolution: Theory and Practice. Wiley, Hoboken (2011)Google Scholar
  4. 4.
    Firth, J.R.: A synopsis of linguistic theory 1930–1955. In: Studies in Linguistic Analysis, pp. 1–32. Philological Society, Oxford (1957)Google Scholar
  5. 5.
    Fortunato, S., Barthélemy, M.: Resolution limit in community detection. Proc. Natl. Acad. Sci. 104(1), 36–41 (2007)CrossRefGoogle Scholar
  6. 6.
    Garimella, K., De Francisci Morales, G., Gionis, A., Mathioudakis, M.: Quantifying controversy in social media. In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, pp. 33–42. ACM (2016)Google Scholar
  7. 7.
    Guerra, P.H.C., Meira Jr., W., Cardie, C., Kleinberg, R.: A measure of polarization on social media networks based on community boundaries. In: ICWSM (2013)Google Scholar
  8. 8.
    Guimera, R., Sales-Pardo, M., Amaral, L.A.N.: Modularity from fluctuations in random graphs and complex networks. Phys. Rev. E 70(2), 025101 (2004)CrossRefGoogle Scholar
  9. 9.
    Harris, Z.: Distributional structure. In: Katz, J. (ed.) Philosophy of Linguistics, pp. 26–47. Oxford University Press, New York (1985)Google Scholar
  10. 10.
    Karypis, G., Kumar, V.: Metis-unstructured graph partitioning and sparse matrix ordering system, version 2.0 (1995)Google Scholar
  11. 11.
    Kim, Y., Chiu, Y.I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: ACL 2014, p. 61 (2014)Google Scholar
  12. 12.
    Lancichinetti, A., Fortunato, S.: Limits of modularity maximization in community detection. Phys. Rev. E 84(6), 066122 (2011)CrossRefGoogle Scholar
  13. 13.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of NIPS (2013)Google Scholar
  14. 14.
    Miller, G., Charles, W.: Contextual correlates of semantic similarity. Lang. Cogn. Process. 6(1), 1–28 (1991)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Morales, A., Borondo, J., Losada, J.C., Benito, R.M.: Measuring political polarization: Twitter shows the two sides of venezuela. Chaos: an Interdisciplinary. J. Nonlinear Sci. 25(3), 033114 (2015)Google Scholar
  16. 16.
    Newman, M.E.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)CrossRefGoogle Scholar
  17. 17.
    Nowak, A., Vallacher, R.R., Bui-Wrzosinska, L., Coleman, P.T.: Attracted to conflict: a dynamical perspective on malignant social relations. In: Understanding social change: Political psychology in Poland, pp. 33–49 (2006)Google Scholar
  18. 18.
    Peixoto, T.P.: Hierarchical block structures and high-resolution model selection in large networks. Phys. Rev. X 4(1), 011047 (2014)Google Scholar
  19. 19.
    Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: sentiment analysis in Twitter. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017). Association for Computational Linguistics, Vancouver, Canada, pp. 502–518. http://www.aclweb.org/anthology/S17-2088
  20. 20.
    Stewart, I., Arendt, D., Bell, E., Volkova, S.: Measuring, predicting and visualizing short-term change in word representation and usage in VKontakte social network. In: Proceedings of the 11th International AAAI Conference on Web and Social Media (ICWSM 2017), pp. 33–42 (2017)Google Scholar
  21. 21.
    Volkova, S., Chetviorkin, I., Arendt, D., Durme, B.V.: Contrasting public opinion dynamics and emotional response during crisis. In: SocInfo (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Anna Rumshisky
    • 1
  • Mikhail Gronas
    • 2
  • Peter Potash
    • 1
  • Mikhail Dubov
    • 3
  • Alexey Romanov
    • 1
  • Saurabh Kulshreshtha
    • 1
  • Alex Gribov
    • 1
  1. 1.Department of Computer ScienceUniversity of Massachusetts LowellLowellUSA
  2. 2.Department of RussianDartmouth CollegeHanoverUSA
  3. 3.Higher School of EconomicsMoscowRussia

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