The digital activism and resistance witnessed in Iran go to the heart of my dissertation research, which asks whether the information revolution empowers coercive regimes at the expense of resistance movements or vice versa? Iran is one of my case studies for my upcoming field research in addition to Burma, Tunisia and Ukraine.
There have been a number of excellent blog posts on the intersection between technology and resistance in Iran, and especially on the use of Twitter. The mainstream press is also awash with references to Twitter’s role. For example, Agence France Presse (AFP) recently cited my research in this piece entitled “Twitter Streams Break Iran News Dam.”
However, what I haven’t seen in the blogosphere and mainstream press is the application of an analytical and theoretical framework to place Twitter’s use in Iran into context.
For example, just how important is/was Twitter’s role vis-a-vis the mobilization and organization of anti-government protests in Iran? We can draw on anecdotes here and there but this process is devoid of any applied social science methodology.
This post seeks to shed light on how, when and why information and communication technologies (ICTs) are used by resistance movements in repressive environments. The framework I draw on (summarized below) is informed by Kelly Garrett’s excellent publication on “Protest in an Information Society: A Review of the Literature on Social Movements and New ICTs” (2006).
The framework seeks to “explain the emergence, development and outcomes of social movements by addressing three interrelated factors: mobilizing structures, opportunity structures and framing processes” within the context of ICTs. (The figure below is excerpted from my dissertation, hence the figure 4 reference).
- Mobilizing Structures are the mechanisms that facilitate organization and collective action. These include social structures and tactical repertoires.
- Opportunity Structures are conditions that favor social movement activity. For example, these include factors such as the state’s capacity and propensity for repression.
- Framing Processes are “strategic attempts to craft, disseminate, and contest the language and narratives used to describe a movement.”
These three factors should be further disaggregated to facilitate analysis. For example, mobilizing structures can be divided into categories susceptible to the impact of ICTs:
- Participation levels (recruitment);
- Contentious activity;
- Organizational issues.
These sub-indicators are still to broad, however. Take, for example, participation levels; what is participation a function of? What underlying mechanisms are facilitated or constrained by the wider availability and use of ICTs? Participation levels may change as a function of three factors:
- Reduction of participation costs;
- Promotion of collective identity;
- Creation of community.
These activities are of course not mutually exclusive but often interdependent. In any case, taking the analysis of ICTs in repressive environments to the tactical level facilitates the social science methodology of process tracing.
We can apply the above framework to test a number of hypotheses regarding Twitter’s use in Iran. Take Mobilizing Structures, for example. The following hypothesis could be formulated.
- Hypothesis 1: The availability of Twitter in Iran increased participation levels, contentious activity and organizational activity.
Using process tracing and the above framework, one could test hypothesis 1 as follows:
These causal chains, or “micro theories,” are posited with the “⎥” marker to signify that the causal relationship is contended. The direction of the arrows above reflects the theoretical narratives extracted from the theoretical framework presented above. Note that the above “micro” theories are general and not necessarily reflective of Twitter’s use in Iran.
Iran Case Study
When the arrows are tallied, the results suggest the following general theory: there is a direct and positive relationship between the impact of Twitter and the incidents of protests and riots. The next step is to test these “micro theories” in the context of Iran by actually “weighting” the arrows. And of course, to do so comparatively by testing the use of Twitter relative to the use of mobile phones and the Internet. Furthermore, the results of this hypothesis testing should be compared to those for Opportunity Structures and Framing Processes.
I plan to carry out field research to qualitatively test these hypotheses once the first phase of my dissertation is completed. The first phase is a large-N quantitative study to determine whether increasing access to ICTs in repressive regimes is a statistically significant predictor of anti-government protests.
Patrick Philippe Meier