Berkman’s social network analysis reveals four major network clusters (with identifiable sub-clusters) in the Iranian blogosphere. The authors have labeled the four clusters as 1) Secular / Reformist, 2) Conservative / Religious, 3) Persian Poetry and Literature, and 4) Mixed Networks.
Surprisingly, a minority of bloggers in the secular/reformist pole appear to blog anonymously, even in the more politically-oriented part of it; instead, it is more common for bloggers in the religious/conservative pole to blog anonymously.
Blocking of blogs by the government is less pervasive than we had assumed. Most of the blogosphere network is visible inside Iran, although the most frequently blocked blogs are clearly those in the secular/reformist pole. Given the repressive media environment in Iran today, blogs may represent the most open public communications platform for political discourse. The peer-to-peer architecture of the blogosphere is more resistant to capture or control by the state than the older, hub and spoke architecture of the mass media model.
So are we likely to witness iRevolutions in Iran?
In authoritarian regimes, networked communications can allow participants to get around state control. As an example, Radio B92 in Serbia simply broadcast through the Internet after the government attempted to shut it down. In Iran, satellite TV, Internet based radio stations, cell phones, and other Internet based tools are difficult if not impossible for the regime to control. Costs are generally high for regimes that limit access and connectivity. The Internet will not lead automatically to liberal, open public spheres in authoritarian regimes, but it will make it harder to control and more costly for authoritarian states to do so. […]
Early conventional wisdom held that bloggers were all young democrats critical of the regime, but we found conversations including politics, human rights, poetry, religion, and pop culture. Given the repressive media environment and high profile arrests and harassment of bloggers, one might not expect to find much political contestation taking place in the Iranian blogosphere. And yet oppositional discourse is robust. […]
In conclusion, the authors essentially pose the same question that I am exploring for my dissertation:
The question at hand is not whether the Iranian blogosphere provides a Samizdat to the regime’s Politburo, but whether the new infrastructure of the social nervous system, which is changing politics in the US and around the world, will also change politics in Iran, and perhaps move its hybrid authoritarian/democratic system in a direction that is more liberal in the sense of modes of public discourse, if not necessarily in a direction that is more liberal in the sense of political ideology.
Berkman’s next step should be to move from static network analysis to dynamic analysis. The topology of the network itself over time should reveal other interesting insights. I would recommend they look up Mark Newman at the Santa Fe Institute. Another software program for networks analysis that I would suggest they use is one used to model foodweb dynamics in 3D. This clip demonstrates the program’s features.
Update: I just met with Josh Goldstein, a researcher at the Berkman Center who contributed to this study. Josh was interested in getting more of my thoughts on possible next steps regarding future research using social network analysis (SNA). I suggested they track network parameters (such as degree centrality) over time and find explanations for changes over time. In other words, plot the number of edges that each node (blogger) is connected to over time. For example, how does degree centrality change within the different clusters identified by Berkman after a terrorist events, i.e, events exogenous to the network? Recent research suggests that blogs display a power law relationship between frequency and magnitude, i.e., there are many nodes with few edges, and few nodes with many edges. Does the Persian blogosphere follow this distribution? Why or why not? Does the slope of the power law distribution become flatter or steeper following crises events? Again, why or why not? What social science explanations account for changes in network topologies over time?