Arab Spring: from newspaper

  • Kenneth Joseph
  • Kathleen M. Carley
  • David Filonuk
  • Geoffrey P. Morgan
  • Jürgen Pfeffer
Original Article


Agent-based simulation models are an important methodology for explaining social behavior and forecasting social change. However, a major drawback to using such models is that they are difficult to instantiate for specific cases and so are rarely reused. We describe a text-mining network analytic approach for rapidly instantiating a model for predicting the tendency toward revolution and violence based on social and cultural characteristics of a large collection of actors. We illustrate our approach using an agent-based dynamic network framework, Construct, and newspaper data for the 16 countries associated with the Arab Spring. We assess the overall accuracy of the base model across independent runs for 20 different months during the Arab Spring, observing that although predictions led to several false positives, the model is able to predict revolution before it occurs in three of the four nations in which the government was successfully overthrown.


Agent-based modeling Social simulation Model instantiation Arab Spring 



This work was supported in part by the Air Force Office of Sponsored Research, FA9550-11-1-0179 and the Office of Naval Research through a Minerva N000141310835. Additional support was provided by the center for Computational Analysis of Social and Organizational Systems and the Institute for Software Research at Carnegie Mellon University. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the National Science Foundation, or the U.S. government.


  1. Buskens V, Yamaguchi K (2002) A new model for information diffusion in heterogeneous social networks. Soc Methodol 29(1):281–325CrossRefGoogle Scholar
  2. Campante FR, Chor D (2012) Why was the Arab world poised for revolution? schooling, economic opportunities, and the Arab spring. J Econ Perspect 26(2):167–187CrossRefGoogle Scholar
  3. Carley K (1990) Coordinating for Success: Trading Information Redundancy for Task Simplicity. Technical Report, Institute for Software Research, Carnegie Mellon UniversityGoogle Scholar
  4. Carley K (1991) A theory of group stability. Am Soc Rev 56(3):331–354CrossRefGoogle Scholar
  5. Carley KM (2002) Smart agents and organizations of the future. In: The handbook of new media, vol 12, pp 206–220Google Scholar
  6. Carley KM, Martin MK, Hirshman BR (2009) The etiology of social change. Topics Cogn Sci 1(4):621–650CrossRefGoogle Scholar
  7. Carley KM, Filonuk DT, Joseph K, Kowalchuck M, Lanham MJ, Morgan GP (2012) Construct User Guide. Technical Report, Institute for Software Research, Carnegie Mellon UniversityGoogle Scholar
  8. Carley KM, Pfeffer J, Reminga J, Storrick J, Columbus D (2012) ORA User’s Guide 2012. DTIC DocumentGoogle Scholar
  9. Carley KM, Morgan G, Lanham M, Pfeffer J (2012c) Multi-modeling and socio-cultural complexity: reuse and validation. Adv Design Cross Cult Act 2:128Google Scholar
  10. Carpineto C, Romano G (2012) A survey of automatic query expansion in information retrieval. ACM Comput Surv 44(1):1:1–1:50CrossRefGoogle Scholar
  11. Centola D, Michael M (2007) Complex contagions and the weakness of long ties. Am J Soc 113(3):702–734CrossRefGoogle Scholar
  12. Diesner J, Carley KM (2008) Conditional random fields for entity extraction and ontological text coding. Comput Math Organ Theory 14(3):248–262CrossRefzbMATHGoogle Scholar
  13. Diesner J, Frantz TL, Carley KM (2005) Communication networks from the Enron email corpus ‘it’s always about the people. Enron is no different. Comput Math Organ Theory 11(3):201–228CrossRefzbMATHGoogle Scholar
  14. Eisenstein J, O’Connor B, Smith NA, Xing EP (2010) A latent variable model for geographic lexical variation. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, EMNLP’10. Association for Computational Linguistics. Stroudsburg, PA, USA, pp 1277–1287Google Scholar
  15. Epstein JM, Axtell R (1996) Growing artificial societies: social science from the bottom up. Brookings Institution Press, Washington, D.C.Google Scholar
  16. Finkel JR, Grenager T, Manning C (2005) Incorporating non-local information into information extraction systems by Gibbs sampling. In: Proceedings of the 43rd annual meeting on association for computational linguistics, ACL’05. Association for Computational Linguistics, Stroudsburg, pp 363–370Google Scholar
  17. Fraley C, Raftery AE, Murphy TB, Scrucca L (2012) Mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical ReportGoogle Scholar
  18. Gilbert N (2007) Agent-based models. Sage Publications Inc, Thousand OaksGoogle Scholar
  19. Greenwald AG, Banaji MR (1995) Implicit social cognition: attitudes, self-esteem, and stereotypes. Psychol Rev 102(1):4–27CrossRefGoogle Scholar
  20. Hamdy N, Gomaa EH (2012) Framing the Egyptian uprising in Arabic language newspapers and social media. J Commun 62 (2)Google Scholar
  21. Hilton JL, von Hippel W (1996) Stereotypes. Annu Rev Psychol 47(1):237–271CrossRefGoogle Scholar
  22. Hyndman RJ, Fan Y (1996) Sample quantiles in statistical packages. Am Stat 50(4):361–365Google Scholar
  23. Joseph K, Morgan GP, Carley KM (2014) On the coevolution of stereotype, culture and social relationships. Soc Sci Comput RevGoogle Scholar
  24. Kossinets G, Watts DJ (2009) Origins of homophily in an evolving social network 1. Am J Soc 115(2):405–450CrossRefGoogle Scholar
  25. Krivitsky PN, Handcock MS, Raftery AE, Hoff PD (2009) Representing degree distributions, clustering, and homophily in social networks with latent cluster random effects models. Soc Netw 31(3):204–213CrossRefGoogle Scholar
  26. Lanham MJ, Morgan GP, Carley KM (2014) Social network modeling and agent-based simulation in support of crisis De-escalation. IEEE Trans Human Mach SystGoogle Scholar
  27. Lazarsfeld PF, Merton RK (1954) Friendship as a social process: a substantive and methodological analysis. In: Freedom and Control in Modern Society. Van Nostrand, pp 18–66Google Scholar
  28. Li Y, Duan H, Zhai CX (2012) A generalized hidden Markov model with discriminative training for query spelling correction. In: Proceedings of the 35th international ACM SIGIR conference on research and development in information retrieval, pp 611–620Google Scholar
  29. Licoppe C (2004) Connected presence: the emergence of a new repertoire for managing social relationships in a changing communication technoscape. Environ Plan D Soc Space 22:135–156CrossRefGoogle Scholar
  30. Martin MK, Pfeffer J, Carley KM (2013) Network text analysis of conceptual overlap in interviews, newspaper articles and keywords. Soc Netw Anal Min, pp 1–13Google Scholar
  31. McPherson JM, Ranger-Moore JR (1991) Evolution on a dancing landscape: organizations and networks in dynamic Blau space. Soc Forces 70(1):19–42CrossRefGoogle Scholar
  32. McPherson M, Lovin L, Cook J (2001) Birds of a feather: homophily in social networks. Annu Rev Soc 1:415–444CrossRefGoogle Scholar
  33. Mead GH (1925) The genesis of the self and social control. Int J Ethics 35(3):251–277Google Scholar
  34. Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2(1–2):1–135CrossRefGoogle Scholar
  35. Papacharissi Z, de Fatima Oliveira M (2012) Affective news and networked publics: the rhythms of news storytelling on #Egypt. J Commun 62(2):266–282CrossRefGoogle Scholar
  36. Pfeffer J, Carley KM (2012) Rapid modeling and analyzing networks extracted from pre-structured news articles. Comput Math Organ Theory 18(3):280–299CrossRefGoogle Scholar
  37. Pfeffer J, Carley KM (2013) The importance of local clusters for the diffusion of opinions and beliefs in interpersonal communication networks. Int J Innov Technol Manag 10(5)Google Scholar
  38. Raina R, Ng AY, Koller D (2006) Constructing informative priors using transfer learning. In: Proceedings of the 23rd international conference on machine learning, pp 713–720Google Scholar
  39. Rogers EM (2003) Diffusion of Innovations, 5th edn. Simon and Schuster, New YorkGoogle Scholar
  40. R Core Team (2012) R: a language and environment for statistical computing. Vienna, Austria.
  41. Schreiber C, Carley KM (2012) Validating agent interactions in construct against empirical communication networks using the calibrated grounding technique. IEEE Trans Syst Man Cybern Part A Syst Humans 99:1–9Google Scholar
  42. Tajfel H, Turner JC (1979) An integrative theory of intergroup conflict. In: Austin W Worche S (eds) The social psychology of intergroup relations. Brooks/Cole, Monterey, pp 33–47Google Scholar
  43. Wasserman L (2003) All of statistics: a concise course in statistical inference. Springer, BerlinGoogle Scholar
  44. Wegner DM (1995) A computer network model of human transactive memory. Soc Cognit 13(3):319–339CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Wien 2014

Authors and Affiliations

  • Kenneth Joseph
    • 1
  • Kathleen M. Carley
    • 1
  • David Filonuk
    • 1
  • Geoffrey P. Morgan
    • 1
  • Jürgen Pfeffer
    • 1
  1. 1.Computation, Organizations and Society Program, School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations