Advertisement

Online Collective Action and the Role of Social Media in Mobilizing Opinions: A Case Study on Women’s Right-to-Drive Campaigns in Saudi Arabia

  • Nitin AgarwalEmail author
  • Merlyna Lim
  • Rolf T. Wigand
Part of the Public Administration and Information Technology book series (PAIT, volume 1)

Abstract

With the advent of advanced information and communication technologies (ICT), especially social media, new forms of collective actions (CAs) have emerged calling for reassessment of some aspects of traditional CA theories. However, regardless of the prominent role of social media platforms in various movements, there is a scarcity of online CA research. Existing computational studies focusing on capturing and mapping social media interactions and issues manage to identify the very manifestations of CA. These studies, unfortunately, rarely go beyond a mere descriptive tendency. We propose a methodology to gain deeper insights into online CA by analyzing issue propagation, influential community members’ roles, and the transcending nature of CA through individual, community, and transnational perspectives. The efficacy of the proposed model is demonstrated by a case study of the Saudi women campaigns on the ban of driving, including data from the 2008 Wajeha al-Huwaider’s driving campaigns and the 2011 ‘Women2Drive’ campaign observed on various social media sites. As conceptualized, utilized, and illustrated in this case study, our proposed methodology highlights several key contributions to the fundamental research on online CAs as well as computational studies on social media in general. We offer: a new framework to understand the evolution and diffusion of issues in online CA networks; a new approach focusing on the formation of issues providing a rigorous model; and, ultimately, a new understanding of the relationship between online CAs and the rapidly changing online environment.

Keywords

Social Medium Collective Action Saudi Arabia Social Media Site Influence Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This research was funded in part by the National Science Foundation’s Social-Computational Systems (SoCS) and Human Centered Computing (HCC) programs (Award Numbers: IIS-1110868 and IIS-1110649) and the US Office of Naval Research (Grant number: N000141010091). Their support is gratefully acknowledged. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies.

References

  1. Adamic, L., & Glance, N. (2005). The political blogosphere and the 2004 US election. Proceedings of the Third International Workshop on Link Discovery, 36–43.Google Scholar
  2. Agarwal, N., Galan, M., Liu, H., & Subramanya, S. (2010). WisColl: Collective wisdom based blog clustering. Journal of Information Science, 180, 39–61.Google Scholar
  3. Agarwal, N., Lim, M., & Wigand, R. T. (2011a). Finding her master’s voice: The power of collective action among female muslim bloggers. Proceedings of the 19th European Conference on Information Systems (ECIS). Helsinki, Finland, June 9–11, Paper 74. http://aisel.aisnet.org/ecis2011/74.
  4. Agarwal, N., Lim, M., & Wigand, R. T. (2011b). Collective Action Theory meets the blogosphere: A new methodology. In S. Fong (Ed.), Networked Digital TechnologiesThird International Conference, NDT 2011, Proceedings. Macau, China, July 2011. Berlin—Heidelberg: Springer Verlag, 224–239.Google Scholar
  5. Agarwal, N., & Liu, H. (2009). Modeling and data mining in blogosphere. Bonita Springs, FL: Morgan & Claypool Publishers.Google Scholar
  6. Agarwal, N., Liu, H., Tang, L., & Yu, P. (2008). Identifying influential bloggers in a community. Proceedings of the First International Conference on Web Search and Data Mining (WSDM), February 10–12, Stanford, CA: Stanford University, 207–218.Google Scholar
  7. Anderson, C. (2006). The long tail: Why the future of business is selling less of more. New York: Hyperion Books.Google Scholar
  8. Bimber, B., Flanagin, A. J., & Stohl, C. (2005). Reconceptualizing collective action in the contemporary media environment. Communication Theory, 15, 365–388.Google Scholar
  9. Bob, C. (2005). The marketing of rebellion: Insurgents, media, and international activism. Cambridge, MA: University Press.Google Scholar
  10. Cleaver, H. (1998). The Zapatistas and the electronic fabric of struggle. In J. Holloway, E. Pelaez and E. Pelaez (Eds.) Zapatista!: Reinventing Revolution in Mexico. London: Pluto Press, 81–103.Google Scholar
  11. Coombs, M., Ulicny, B., Jaenisch, H., Handley, J., & Faucheux, J. (2008). Formal analytic modeling of bridge blogs as personal narrative: A case study in grounding interpretation. Proceeding of the Workshop on Social Computing, Behavioral Modeling, and Prediction (SBP), Phoenix, AZ, 207–217.Google Scholar
  12. Etling, B., Kelly, J., Faris, R., & Palfrey, J. (2009). Mapping the Arabic blogosphere: Politics, culture, and dissent. Internet & Democracy Project, Berkman Center for Internet & Society. Cambridge, MA: Harvard University.Google Scholar
  13. Etzioni, A., & Etzioni, O. (1999). Face-to-face and computer-mediated communities, a comparative analysis. The Information Society, 15, 241–248.Google Scholar
  14. Faloutsos, M., Faloutsos, P., & Faloutsos, C. (1999). On power-law relationships of the Internet topology. ACM SIGCOMM Computer Communication Review, 29, 251–262.Google Scholar
  15. Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3–5), 75–174.CrossRefGoogle Scholar
  16. Friedland, J., & Rogerson, K. (2009) How political and social movements form in the Internet and how they change over time. IHSS Reports, Institute for Homeland Security Solutions, Research Triangle Park, NC.Google Scholar
  17. Gill, K. E. (2004). How can we measure the influence of the blogosphere? Paper presented at WWW2004, New York.Google Scholar
  18. Goldenberg, J., Libai, B., & Muller, E. (2001). Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters, 12, 211–223.Google Scholar
  19. Goyal, A., Bonchi, F., & Lakshmanan, L. V. S. (2010). Learning influence probabilities in social networks. Proceedings of the Third International Conference on Web Search and Data Mining, 241–250.Google Scholar
  20. Gruhl, D., Guha, R., Liben-Nowell, D., & Tomkins, A. (2004). Information diffusion through blogspace. Proceedings of the 13th International Conference on the World Wide Web, 491–501.Google Scholar
  21. In The News. (2012, February 6). Manal al-Sharif. Arkansas Democrat-Gazette (Little Rock, AR), 1A.Google Scholar
  22. Java, A., Joshi, A., & Finin, T. (2008). Detecting communities via simultaneous clustering of graphs and folksonomies. Proceedings of the Tenth Workshop on Web Mining and Web Usage Analysis (WebKDD).Google Scholar
  23. Java, A., Kolari, P., Finin, T., & Oates, T. (2006). Modeling the spread of influence on the blogosphere. Proceedings of the 15th International World Wide Web Conference, May 22–26, Edinburgh, UK.Google Scholar
  24. Kelly, J., & Etling, B. (2008). Mapping Iran’s online public: Politics and culture in the Persian blogosphere (Vol. 1). Berkman Center for Internet & Society. Cambridge, MA: Harvard University.Google Scholar
  25. Kritikopoulos, A. Sideri, M., & Varlamis, I. (2006). Blogrank: ranking weblogs based on connectivity and similarity features. Proceedings of the 2nd international Workshop on Advanced Architectures and Algorithms for Internet Delivery and Applications. Pisa, Italy, October 10—10, AAA-IDEA ‘06.Google Scholar
  26. Lancichinetti, A., & Fortunato, S. (2009). Community detection algorithms: A comparative analysis. Physical Review E, 80, 056117.Google Scholar
  27. Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., & Glance, N. (2007). Cost- effective outbreak detection in networks. Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 420–429.Google Scholar
  28. Li, B., Xu, S., & Zhang, J. (2007). Enhancing clustering blog documents by utilizing author/reader comments. Proceedings of the 45th Annual Southeast Regional Conference, New York, 94–99.Google Scholar
  29. Lim, M. (2012). Clicks, Cabs, and Coffee Houses: Social Media and Oppositional Movements in Egypt (2004–2011). Journal of Communication, 62(2), 231–248.Google Scholar
  30. Lim, M. (2009). Global Muslim blogosphere: Mosaics of global-local discourses. In M. McLelland and G. Goggin (Eds.) Internationalizing Internet Studies: Beyond Anglophone Paradigms. London: Routledge, 178–195. Google Scholar
  31. Lim, M. (2006). Cyber-urban activism and the political change in Indonesia. Eastbound, 1, 1–21.Google Scholar
  32. Lim, M. (2004). Informational terrains of identity and political power: the Internet in Indonesia. Indonesian Journal of Social and Cultural Anthropology, 27, 1–11.Google Scholar
  33. Lupia, A., & Sin, G. (2003). Which public goods are endangered?: How evolving communication technologies affect the logic of collective action. Public Choice, 117, 315–331.Google Scholar
  34. Margetts, H., John, P., Escher, T., & Reissfelder, E. (2011). Social information and political participation on the internet: An experiment. European Political Science Review, 3(3), 321–344.CrossRefGoogle Scholar
  35. Margetts, H., John, P., Escher, T., & Reissfelder, S. (2009). Can the internet overcome the logic of collective action? An experiment of the impact of social pressure on political participation. Political Studies Association Annual Conference, April 7–9, Manchester, UK: University of Manchester.Google Scholar
  36. Markus, M.L., Steinfield, C.W., Wigand, R.T. & Minton, G. (2006). Industry-wide IS standardization as collective action: The case of the US residential mortgage industry. MIS Quarterly, 30, 439–465.Google Scholar
  37. McAdam, D. (1996). The framing function of movement tactics: Strategic dramaturgy in the American civil rights movement. In D. McAdam, J. D. McCarthy & M. N. Zald (Eds.), Comparative Perspectives on Social Movements: Political Opportunities, Mobilizing Structures, and Cultural Framings. New York: Cambridge University Press, 338–356.Google Scholar
  38. Olson, M. (1965). The logic of collective action. Cambridge, MA: Harvard University Press.Google Scholar
  39. Pareto, V. (1935). The Mind and Society. London: Jonathan Cape.Google Scholar
  40. Richardson, M., & Domingos, P. (2002). Mining knowledge-sharing sites for viral marketing. Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 61–70.Google Scholar
  41. Sandler, T. (1992). Collective action: Theory and applications. Volume 4. Ann Arbor, MI: University of Michigan Press.Google Scholar
  42. Song, X., Chi, Y., Hino, K., & Tseng, B. (2007). Identifying opinion leaders in the blogosphere. Proceedings of the Sixteenth ACM Conference on information and Knowledge Management, 971–974.Google Scholar
  43. Wigand, R. T., Steinfield, C.W. and Markus, M.L. (2005). Exploring interorganizational systems at the industry level of analysis: Evidence from the US home mortgage industry. Journal of Information Technology, 20, 224–233.Google Scholar
  44. Wheelan, C. (2011). Introduction to public policy. New York: W.W. Norton.Google Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Department of Information ScienceUniversity of Arkansas at Little RockLittle RockUSA
  2. 2.Consortium of Science, Policy and Outcomes (CSPO), School of Social Transformation—Justice and Social InquiryArizona State UniversityTempeUSA

Personalised recommendations