Abstract
The causes of religious violence have attracted numerous explanations in the years since the 9/11 attacks on the Pentagon and the World Trade Towers. However, most forms of religious extremism do not result in violence (e.g., the Amish, Hasidim, Jains) and religious groups have not cornered the market on egregious violence. Nevertheless, religious violence does occur, and this paper examines the interplay of social networks and religious violence. It builds on Cass Sunstein’s “law of group polarization,” which predicts that when like-minded people deliberate as an organized group, the general opinion shifts toward extreme versions of their common belief. It argues that internally dense religious groups that maintain few ties to the wider society are more likely to embrace extreme views and behavior than are those that are not as dense and/or remain tied to the wider society. The argument is then tested using social network analysis methodologies to examine the evolution of the Hamburg Cell, which played a critical role in the 9/11 terrorist attacks. It concludes with a series of policy recommendations that can limit but not eliminate religious extremism and violent behavior in the future.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Moreover, non-religious groups can be just as violent as religious ones, with Sri Lanka’s LTTE (Liberation Tigers of Tamil Elam), Colombia’s FARC (Fuerzas Armadas Revolucionarias de Colombia—Revolutionary Armed Forces of Colombia), and Turkey’s PKK (Partiya Karkerên Kurdistan—Kurdish Worker’s Party) serving as three prominent examples.
Technically, in White’s view of meaning comes from switching between networks (Steiny 2007).
Yousef attempted to enlist Ishtiaque Parker, a South African student to whom Yousef’s brother-in-law introduced him. At first, Yousef shared little with Parker, but eventually he told him about his involvement in the World Trade Center and convinced Parker to transport a bag overseas for him. Later, Yousef sent Parker to an airport with explosive-packed suitcases with instructions to place them on a U.S. carrier. Parker did not go through with the plan, lying to Yousef that airport security was too tight. Yousef also told Parker that Philippine authorities had confiscated Yousef’s computer, which had Parker’s name in it. This frightened Parker, and when Yousef asked him to take a small package to a Shiite mosque, Parker called the U.S. Embassy. This led to Yousef’s arrest and extradition to the U.S where he is now serving two life sentences.
Scott Atran, email communication, October 7, 2014.
Longitudinal social network datasets that track groups both prior to and during their radicalization period are rare. The Hamburg Cell dataset is one of the few, which is why it is used here.
The JJATT was sponsored by the Air Force Office of Scientific Research (AFOSR) and developed under the guidance Scott Atran (PI). Although there are some limitations to the data (see Gerdes 2015), this is not unusual with data on covert and illegal networks (Krebs 2002; Sparrow 1991) Nevertheless, the data appear to accurately reflect what scholars have learned about the cell (9/11 Commission 2004; Sageman 2004).
For the years 2000 and 2001, the data actually include two times points.
Network measures were calculated using UCINET (Borgatti et al. 2002). It is generally preferable to estimate multiple measures because just as no single metric can capture an economy’s performance (e.g., gross domestic product, unemployment, poverty rate, inflation), no single social network measure captures a network’s interconnectedness. The appendix details how each of the measures is calculated. If data on external ties were available, Krackhardt’s (1994) E-I index, which compares the ratio of internal and external ties and is an excellent measure of a group’s insularity, could be calculated.
Standard tests do not apply because the nature of social network data violates their assumptions (Borgatti et al. 2013: 125–126). For example, standard statistical models assume the independence of observations, but a central assumption of social network analysis is that observations (i.e., actors) are interdependent and these interdependencies affect behavior (Azarian 2005; Wasserman and Faust 1994). Also, standard models assume that the population variables follow a known statistical distribution (e.g., normal, logistic), but the distribution of social network data is seldom normal and often unknown. Finally, and perhaps most relevant here, social network data are not a random sample of observations. There is no larger population to which to generalize. The network is the population, and as such the estimated metrics do not reflect the means of 100s or 1000s of observations but rather the population itself. In other words, the density for 1998 is not the average of, say, 400 observations; it is the density a single network, the 1998 Hamburg network. Thus, significance tests of the differences in means do not apply here. This is why it is quite common for papers analyzing social networks to only calculate and present descriptive statistics of networks. See, for example, the recent papers analyzing the change in a network of UK Suffragettes over time (Crossley et al. 2012).
Here, structural similarity refers to actors who share the same or similar patterns of ties to others and is often used to express similarities in tie patterns that result from similar job functions (e.g., middle managers are expected to have patterns of ties that are similar to one another).
SOAMs can also include variables that measure the effect that exogenous factors (i.e., attribute data), such as race, gender, religious affiliation, and age, have on tie formation. The JJATT data on the Hamburg Cell do not include any attribute data on the network members. This is not a problem here since what the models tested was whether the Hamburg Cell displayed a (statistically significant) propensity toward network closure. That is clearly the case. It is possible that its propensity is also a function of other social processes such as homophily (e.g., similar age, nationality), and their inclusion in the models would reduce the effect of the alternating triangles. That, however, would not alter the fact that over time the Hamburg Cell became increasingly interconnected.
Unbeknownst to the cell, German authorities had inserted a microphone in the apartment and occasionally monitored their conversations.
The other four members of the cell went to Afghanistan for training in the spring of 2000.
Interestingly, in the lead up to their attack on the offices of Charlie Hebdo that left 12 people dead, Chérif and Saïd Kouachi also took steps to conceal their extremist beliefs (Callimachi and Yardley 2015).
A model that tested the network’s evolution from 1995 to 1996 did not converge and thus its results are not included in the table.
It should be noted that p values are not calculated for the rate parameters because a value of zero would indicate that no changes were made and thus testing whether they were zero makes no sense (Borgatti et al. 2013: 146).
The two-year delay between the group’s radicalization and the 9/11 attacks, of course, reflects the time it took for its members to travel to Afghanistan to be trained and then return to Germany and then move to the United States in order to plan and carry out the operation.
See the University of Manchester’s Covert Network Project: http://www.socialsciences.manchester.ac.uk/research/research-centres-and-networks/mitchell-centre/our-research/covert-networks/.
References
9/11 Commission. 2004. The 9/11 Commission Report. New York: W. W. Norton & Company.
Alimi, Eitan Y. 2011. Relational dynamics in factional adoption of terrorist attacks: A comparative perspective. Theory and Society 40: 95–118.
Alimi, Eitan Y., Lorenzo Bosi, and Chares Demetriou. 2012. Relational dynamics and processes of radicalization: A comparative framework. Mobilization: An International Journal 17: 7–26.
Azarian, G.Reza. 2005. The general sociology of Harrison C. White: Chaos and order in networks. New York: Palgrave Macmillan.
Borgatti, Stephen P., Kathleen M. Carley, and David Krackhardt. 2006. On the robustness of centrality measures under conditions of imperfect data. Social Networks 28: 124–136.
Borgatti, Stephen P., Martin G. Everett, and Linton C. Freeman. 2002. UCINET for windows: Software for social network analysis. Harvard, MA: Analytical Technologies.
Borgatti, Stephen P., Martin G. Everett, and Jeffrey C. Johnson. 2013. Analyzing social networks. Thousand Oaks, CA: Sage.
Bourdieu, Pierre. 1987. Legitimation and structured interests in Weber’s sociology of religion. In Max Weber, rationality and modernity, ed. S. Whimster, and S. Lash, 119–136. Boston, MA: Allen and Unwin.
Brym, Robert J., and Bader Araj. 2006. Suicide bombing as strategy and interaction. Social Forces 84: 1969–1986.
Callimachi, Rukmini, and Jim Yardley. 2015. From scared amateur to Paris Slaughterer. in The New York Times, vol. 154, 1, 14. New York: The New York Times.
Collins, Randall. 2004. Interaction ritual chains. Princeton, NJ: Princeton University Press.
Collins, Randall. 2008. Violence: A micro-sociological theory. Princeton, NJ: Princeton University Press.
Crossley, Nick, Gemma Edwards, Ellen Harries, and Rachel Stevenson. 2012. Covert social movement networks and the secrecy-efficiency trade off: The case of the UK suffragettes (1906–1914). Social Networks 34: 634–644.
Csárdi, Gábor, and Tamás Nepusz. 2006. The igraph software package for complex network research. InterJournal, Complex Systems 1695. http://igraph.org.
Davis, James A. 1967. Clustering and structural balance in graphs. Human Relations 20: 181–187.
de Nooy, Wouter, Andrej Mrvar, and Vladimir Batagelj. 2011. Exploratory social network analysis with Pajek. Cambridge: Cambridge University Press.
Eggert, Nina. 2014. The impact of political opportunities on interorganizational networks: A comparison of migrants’ organizational fields. Mobilization: An International Journal 19: 369–386.
Eisner, Manuel. 2003. Long-term historical trends in violent crime. Crime and Justice 30: 83–142.
English, Richard. 2004. Armed struggle: The history of the IRA. London: Pan Books.
Fraser, Giles. 2014. To Islamic state, Dabiq is important—But it’s not the end of the world. In The Guardian: Guardian News and Media Limited.
Gerdes, Luke M. 2015. Dark dimensions: Classifying relationships among clandestine actors. In Illuminating dark networks: The study of clandestine groups and organizations, ed. L.M. Gerdes, 19–38. Cambridge: Cambridge University Press.
Goldman, Marion S. 2011. Cultural capital, social networks, and collective violence. In Violence and new religious movements, ed. J.R. Lewis, 307–323. Oxford: Oxford University Press.
Granovetter, Mark. 1973. The strength of weak ties. American Journal of Sociology 73: 1360–1380.
Gregg, Heather S. 2009. Fighting cosmic warriors: Lessons from the first seven years of the global war on terror. Studies in Conflict & Terrorism 32: 188–208.
Gregg, Heather S. 2014. The path to salvation: Religious violence from the crusades to Jihad. Dulles, VA: Potomac Books.
Grim, Brian J., and Roger Finke. 2010. The price of freedom denied: religious persecution and conflict in the twenty-first century. Cambridge: Cambridge University Press.
Hafez, Mohammed M. 2003. Why Muslims rebel: Repression and resistance in the Islamic world. Boulder, CO: Lynne Rienner Publishers.
Hafez, Mohammed M. 2004. From marginalization to massacres: A political process explanation of violence in Algeria. In Islamic activism: A social movement theory approach, ed. Q. Wiktorowicz, 37–60. Bloomington: Indiana University Press.
Hall, John R. 1987. Gone from the promised land: Jonestown in American cultural history. Piscataway, NJ: Transcation Publishers.
Harris, Jenine K. 2014. An introduction to exponential random graph modeling. London: Sage.
Holland, Paul W., and Samuel Leinhardt. 1976. Local structure in social networks. Sociological Methodology 7: 1–45.
Iannaccone, Laurence R. 1994. Why strict churches are strong. American Journal of Sociology 99: 1180–1211.
Juergensmeyer, Mark. 2001. Terror in the mind of god. Berkeley, CA: University of California Press.
Kleiver, Lonnie D. 1999. Meeting God in garland: A model of religious tolerance. Nova Religio 3: 45–53.
Koskinen, Johan H., Garry L. Robins, Peng Wang, and Philippa E. Pattison. 2013. Bayesian analysis for partially observed network data, missing ties, attributes and actors. Social Networks 35: 514–527.
Kossinets, Gueorgi. 2006. Effects of missing data in networks. Social Networks 28: 247–268.
Krackhardt, David. 1994. Graph theoretical dimensions of informal organizations. In Computational organization theory, ed. K.M. Carley, and M.J. Prietula, 89–111. Hillsdale, NJ: L. Erlbaum Associates.
Krebs, Valdis. 2002. Mapping networks of terrorist cells. Connections 24: 43–52.
Kurzman, Charles. 2011. The missing martyrs: Why there are so few Muslim terrorists. Oxford: Oxford University Press.
Lacina, Bethany, and Nils Petter Gleditsch. 2005. Monitoring trends in global combat: A new dataset of battle deaths. European Journal of Population 21: 145–166.
Lamont, Michéle, and Victor Molnar. 2002. The study of boundaries in the social sciences. Annual Review of Sociology 28: 167–196.
Lofland, John, and Rodney Stark. 1965. Becoming a world-saver: A theory of conversion to a deviant perspective. American Sociological Review 30: 862–875.
Lusher, Dean, Johan H. Koskinen, and Garry L. Robins. 2013. Exponential random graph models for social networks. In Structural analysis in the social sciencies, ed. M. Granovetter. Cambridge: Cambridge University Press.
Marsden, Peter V. 1987. Core discussion networks of Americans. American Sociological Review 52: 122–131.
McAdam, Doug. 1986. Recruitment to high risk activism: The case of freedom summer. American Journal of Sociology 92: 64–90.
McAdam, Doug. 1999 [1982]. Political process and the development of black insurgency, 1930–1970. Chicago: University of Chicago Press.
McAdam, Doug, and Ronnelle Paulsen. 1993. Specifying the relationship between social ties and activism. American Journal of Sociology 99: 640–667.
McLaughlin, John M. 2015. Factors of religious violence and a path to peace. Master’s Thesis. Monterey, CA: Department of Defense Analysis, Naval Postgraduate School.
Passy, Florence. 2003. Social networks matter. But how? In Social movements and networks: Relational approaches to collective action, ed. M. Diani, and D. McAdam, 21–48. Oxford: Oxford University Press.
Pinker, Steven. 2011. The better angels of our nature: Why violence has declined. New York: Viking.
Pitts Jr., William L. 1995. Davidians and Branch Davidians. In Armageddon in Waco: Critical perspectives on the Branch Davidian conflict, ed. S.A. Wright, 20–42. Chicago: Chicago University Press.
Raab, Jörg, and H.Brinton Milward. 2003. Dark networks as problems. Journal of Public Administration Research and Theory 13: 413–439.
Rapaport, David. 1988. Messianic sanctions for terror. Comparative Politics 20: 195–213.
Rapoport, Anatole. 1953a. Spread of information through a population with socio-structural bias I: Assumption of transitivity. Bulletin of Mathematical Biophysics 15: 523–533.
Rapoport, Anatole. 1953b. Spread of information through a population with socio-structural bias II: Various models with partial transitivity. Bulletin of Mathematical Biophysics 15: 535–546.
Rapoport, Anatole, and William J. Horvath. 1961. A study of a large sociogram. Behavioral Science 6: 279–291.
Ripley, Ruth M., Tom A.B. Snijders, Zsófia Boda, András Vörös, and Paulina Preciado. 2015. Manual for RSiena. Oxford: University of Oxford, Department of Statistics, Nuffield College.
Robins, Garry L., Philippa E. Pattison, Yuval Kalish, and Dean Lusher. 2007. An introduction to exponential random graph (p*) models for social networks. Social Networks 29: 173–191.
Roth, Randolph. 2001. Homicide in early modern England, 1549–1800: The need for the quantitative snynthesis. Crime, History & Societies 5: 33–67.
Roth, Randolph. 2009. American homicide. Cambridge, MA: Harvard University Press.
Sageman, Marc. 2004. Understanding terror networks. Philadelphia, PA: University of Pennsylvania Press.
Smith, Christian S., Michael O. Emerson, Sally Gallagher, Paul Kennedy, and David Sikkink. 1998. American evangelicalism: Embattled and thriving. Chicago: The University of Chicago Press.
Snijders, Tom A.B. 2001. The statistical evaluation of social network dynamics. Sociological Methodology 31: 361–395.
Snijders, Tom A.B. 2005. Models for longitudinal network data. In Models and methods in social network analysis, ed. P.J. Carrington, J. Scott, and S. Wasserman, 215–247. New York: Cambridge University Press.
Snijders, Tom A.B., and Stephen P. Borgatti. 1999. Non-parametric standard errors and tests for network statistics. Connections 22: 161–170.
Snijders, Tom A.B., Gerhard G. van de Bunt, and Christian Steglich. 2010. Introduction to stochastic actor-based models for network dynamics. Social Networks 32: 44–60.
Snijders, Tom, and Johan H. Koskinen. 2013. Longitudinal models. In Exponential random graph models for social networks, ed. D. Lusher, J. Koskinen, and G. Robins, 130–140. Cambridge: Cambridge University Press.
Snow, David A., Louis A. Zurcher, and Sheldon Ekland-Olson. 1980. Social networks and social movements: A microstructural approach to differential recruitment. American Sociological Review 45: 787–801.
Sparrow, Malcom K. 1991. the application of network analysis to criminal intelligence: An assessment of the prospects. Social Networks 13: 251–274.
Stark, Rodney, and William Sims Bainbridge. 1980. Networks of faith: Interpersonal bonds and recruitment to cults and sects. American Journal of Sociology 85: 1376–1395.
Stark, Rodney, and William Sims Bainbridge. 1985. The future of religion. Berkeley: University of California Press.
Steiny, Donald. 2007. H. White, Identity and control (2nd ed.), Cambridge University Press, Cambridge (2008). Social Networks 29: 609–616.
Stern, Jessica. 2003. Terror in the name of God: Why religious militants kill. New York: HarperCollins.
Sunstein, Cass R. 2002. The law of group polarization. The Journal of Political Philosophy 10: 175–195.
Sunstein, Cass R. 2003. Why societies need dissent. Cambridge, MA: Harvard University Press.
Sunstein, Cass R. 2009. Going to extremes: How like minds unite and divide. Oxford: Oxford University Press.
Tilly, Charles. 2003. The politics of collective action. Cambridge: Cambridge University Press.
Tilly, Charles. 2004. Trust and rule. Theory and Society 33: 1–30.
Tilly, Charles. 2005a. Terror as strategy and relational process. International Journal of Comparative Sociology 46: 11–32.
Tilly, Charles. 2005b. Trust and rule. Cambridge: Cambridge University Press.
Tilly, Charles, and Sidney Tarrow. 2007. Contentious politics. Boulder, CO: Paradigm Publishers.
Van de Bunt, G.G., M.A.J. Van Duijin, and Tom A.B. Snijders. 1999. Friendship networks through time: An actor-oriented statistical network model. Computational and Mathematical Organization Theory 5: 167–192.
Wasserman, Stanley, and Katherine Faust. 1994. Social network analysis: Methods and applications. Cambridge: Cambridge University Press.
Watts, Duncan J. 1999. Small worlds: The dynamics of networks between order and randomness. Princeton, NJ: Princeton University Press.
Watts, Duncan J., and Steven H. Strogatz. 1998. Collective dynamics of ‘small world’ networks. Nature 393: 409–410.
Weber, Max [1922]. 1978. Economy and society: An outline of interpretive sociology. Berkeley: University of California Press.
White, Harrison C. 1992. Identity and control: A structural theory of social action. Princeton, NJ: Princeton University Press.
White, Harrison C. 2008. Identity and control: How social formations emerge. Princeton, NJ: Princeton University Press.
Wink, Walter. 1992. Engaging the powers. Minneapolis, MN: Augsburg Fortress.
Zablocki, Benjamin D. 1980. Alienation and charisma. New York: Free Press.
Acknowledgments
The author would like to thank the several anonymous reviewers who have helped make this a much better paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Everton, S.F. Social Networks and Religious Violence. Rev Relig Res 58, 191–217 (2016). https://doi.org/10.1007/s13644-015-0240-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13644-015-0240-3