Dynamical Evolution of Anti-social Phenomena: A Data Science Approach

  • Syed Shariq Husain
  • Kiran SharmaEmail author
Part of the New Economic Windows book series (NEW)


Human interactions can be either positive or negative, giving rise to different complex social or anti-social phenomena. The dynamics of these interactions often lead to certain spatio-temporal patterns and complex networks, which can be interesting to a wide range of researchers—from social scientists to data scientists. Here, we use the publicly available data for a range of anti-social and political events like ethnic conflicts, human right violations and terrorist attacks across the globe. We aggregate these anti-social events over time, and study the temporal evolution of these events. We present here the results of several time-series analyses like recurrence intervals, Hurst R/S analysis, etc., that reveal the long memory of these time-series. Further, we filter the data country-wise, and study the time-series of these anti-social events within the individual countries. We find that the time-series of these events have interesting statistical regularities and correlations. Using multi-dimensional scaling technique, the countries are then grouped together in terms of the co-movements with respect to temporal growths of these anti-social events. The data science approaches to studying these anti-social phenomena may provide a deeper understanding about their formations and spreading. The results can help in framing public policies and creating strategies that can check their spread and inhibit these anti-social phenomena.



The authors would like to thank Anirban Chakraborti, Vishwas Kukreti, Arun S. Patel and Hirdesh K. Pharasi for critical discussions and inputs. KS acknowledges the University Grants Commission (Ministry of Human Resource Development, Govt. of India) for her senior research fellowship. SSH and KS acknowledge the support by University of Potential Excellence-II grant (Project ID-47) of JNU, New Delhi, and the DST-PURSE grant given to JNU by the Department of Science and Technology, Government of India.


  1. 1.
    Perc, M., Jordan, J.J., Rand, D.G., Wang, Z., Boccaletti, S., Szolnoki, A.: Statistical physics of human cooperation. Phys. Rep. 687, 1–51 (2017)Google Scholar
  2. 2.
    Schelling, T.: Models of segregation. Am. Econ. Rev. 59(2), 488–93 (1969)Google Scholar
  3. 3.
    Schelling, T.: Dynamic models of segregation. J. Math. Sociol. 1, 143–186 (1971)Google Scholar
  4. 4.
    Lahr, M.M., Rivera, F., Power, R.K., Mounier, A., Copsey, B., Crivellaro, F., Edung, J.E., Fernandez, J.M.M., Kiarie, C., Lawrence, J., Leakey, A., Mbua, E., Miller, H., Muigai, A., Mukhongo, D.M., Van Baelen, A., Wood, R., Schwenninger, J.L., Grn, R., Achyuthan, H., Wilshaw, A., Foley, R.A.: Inter-group violence among early holocene hunter-gatherers of west Turkana, Kenya. Nature 529, 394–398 (2016)Google Scholar
  5. 5.
    Abergel, F., Aoyama, H., Chakrabarti, B.K., Chakraborti, A., Deo, N., Raina, D., Vodenska, I.: Econophysics and Sociophysics: Recent Progress and Future Directions. Springer, Berlin (2017)Google Scholar
  6. 6.
    Castellano, C., Fortunato, S., Loreto, V.: Statistical physics of social dynamics. Rev. Mod. Phys. 81(2), 591 (2009)Google Scholar
  7. 7.
    Chakrabarti, B.K., Chakraborti, A., Chatterjee, A.: Econophysics and Sociophysics: Trends and Perspectives. Wiley-VCH, Germany (2006)Google Scholar
  8. 8.
    Sen, P., Chakrabarti, B.K.: Sociophysics: An Introduction. Oxford University Press, Oxford (2014)Google Scholar
  9. 9.
    Clauset, A., Young, M., Gleditsch, K.S.: On the frequency of severe terrorist events. J. Confl. Resolut. 51(1), 58–87 (2007)Google Scholar
  10. 10.
    Sharma, K., Sehgal, G., Gupta, B., Sharma, G., Chatterjee, A., Chakraborti, A., Shroff, G.: A complex network analysis of ethnic conflcts and human rights violations. Sci. Rep. 7(1), 8283 (2017)Google Scholar
  11. 11.
    Husain, S.S., Sharma, K., Kukreti, V., Chakraborti, A.: Identifying the global terror hubs and vulnerable motifs using complex network dynamics (2018). arXiv:1802.01147
  12. 12.
    Richardson, L.: The Roots of Terrorism. Routledge, London (2013)Google Scholar
  13. 13.
    Cutter, S.L., Richardson, D.B., Wilbanks, T.J.: The Geographical Dimensions of Terrorism. Routledge, London (2014)Google Scholar
  14. 14.
    GDELT - data format codebook v 1.03, as on 25 Aug 2013 (2016).
  15. 15.
    The global database of events, language and tone (GDELT) (2016).
  16. 16.
    Global terrorism database (GTD)-codebook: inclusion criteria and variables, as on 3 Jan 2013 (2018).
  17. 17.
    Global terrorism database (GTD) (2018).
  18. 18.
    Tilak, G.: Studies of the recurrence-time interval distribution in financial time-series data at low and high frequencies (2012)Google Scholar
  19. 19.
    Chicheportiche, R., Chakraborti, A.: Copulas and time series with long-ranged dependencies. Phys. Rev. E 89, 042,117 (2014)Google Scholar
  20. 20.
    Chicheportiche, R., Chakraborti, A.: A model-free characterization of recurrences in stationary time series. Phys. A Stat. Mech. Appl. 474, 312–318 (2017)Google Scholar
  21. 21.
    Santhanam, M., Kantz, H.: Return interval distribution of extreme events and long-term memory. Phys. Rev. E 78(5), 051,113 (2008)Google Scholar
  22. 22.
    Tarnopolski, M.: On the relationship between the hurst exponent, the ratio of the mean square successive difference to the variance, and the number of turning points. Phys. A Stat. Mech. Appl. 461, 662–673 (2016)Google Scholar
  23. 23.
    Chakraborti, A., Santhanam, M.: Financial and other spatio-temporal time series: long-range correlations and spectral properties. Int. J. Mod. Phys. C 16(11), 1733–1743 (2005)Google Scholar
  24. 24.
    Torres, L.R.M., Rojas, A.R., Luévano, J.R., Hernández, R.T.P.: Exponente de hurst en series de tiempo electrosísmicasGoogle Scholar
  25. 25.
    Pharasi, H.K., Sharma, K., Chakraborti, A., Seligman, T.H.: Complex market dynamics in the light of random matrix theory (2018). arXiv:1809.07100
  26. 26.
    Chakraborti, A., Sharma, K., Pharasi, H.K., Das, S., Chatterjee, R., Seligman, T.H.: Characterization of catastrophic instabilities: market crashes as paradigm (2018). arXiv:1801.07213
  27. 27.
    Pharasi, H.K., Sharma, K., Chatterjee, R., Chakraborti, A., Leyvraz, F., Seligman, T.H.: Identifying long-term precursors of financial market crashes using correlation patterns (2018). New J. Phys. 20, 103041 (2018)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computational and Integrative SciencesJawaharlal Nehru UniversityNew DelhiIndia

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