In the last years we have witnessed an explosion of popular uprisings in many countries. It is not the first time that the world witnesses popular unrest, but what is new is the avenues for civil mobilisation used. Online social platforms have been at the centre of protests as channels for free expression, sources of information and tools for coordination.
Social media must be important force if governments strive to curb its freedom . And yet, revolutions and turmoil do not happen in Twitter, nor because of Twitter, just as the printing press did not cause the Protestant Reformation. But the online world is a faithful correlate where scientists mine information within which can gives important clues about these social movements.
This is where Complexity Science (CS) comes in. At the crossroads of Physics, Computer Science and Sociology, CS helps us capture some of the mechanisms and dynamics that have fed the so-called Twitter Revolutions around the globe. To do so, CS exploits the unprecedented availability of data from our ever-growing digital footprint, especially social media sites.
CS tools have given us the opportunity to look at social movements in Spain, Egypt and Brazil from a different perspective. Using Twitter data, we are able to follow how these movements grow, develop, interact and solidify.
Dynamic self-organisation
The first lesson we learn from different examples of social activism on Twitter has to do with their growth and evolution. Unlike top-down designed systems, these networks evolve towards increasing complexity without the intervention of external forces. This means in practice that heterogeneity emerges in such structures, where a few users concentrate most attention (hubs).
Three interesting consequences follow. First, this growth pattern and final structure can be found in different means of communication, such as social media, mobile connections, the internet, etc. That's the reason why an interdisciplinary approach is necessary.
The downside to hub-dominated structures is their weakness in front of attacks: the removal of a few selected users can lead to communication breakdown. And yet, networks stand as adaptive and versatile structures, with great plasticity to rewire connections and regain robustness.
Information flow, cascades and the brain metaphor
This evolution has a number of deep consequences when a protest is brewing. It drastically reduces the cost of communication in terms of time, money, energy, etc. But most importantly, it facilitates the existence of cascading/viral events (anything from a single tweet to a shared video to an actual protest), which can be triggered from just about anywhere .Obviously 'central' users (those with many connections) can trigger them, but so can 'ordinary' users - the true ' hidden influentials'.
Modular structure and polarisation
Activity volume for specific hashtags in Egypt, from Jun to Sept 2013. Above, dominant hashtags in the pro-military intervention group. Below, dominant hashtags for those opposing it. Source: QCRI
A general observation when scientists study complex networks is that these are highly modular(i.e. they are organised in groups or communities) , and political turmoil in social media is not an exception. With the aid of costly algorithms, it is possible to detect dense, cohesive communities: groups of users who connect more often within the community, than across groups.
This information is useful not only to provide an interpretable map of data, as outlining communities can tell you a lot about dynamic barriers between them. It is also important because it provides a useful key to understanding complex systems: Dynamics affect structure (the activity of similarly minded individuals tends to bring them together in the network), and structure constraints dynamics (since connections are organised in groups, activity happens within their boundaries).
The geographic ingredient, diffusion and sensitivity
'Who-steers-whom' summary in the Brazilian protests of 2013. Red colour indicates dominance. Circles size is proportional to the activity recorded in that area.The largest demonstration took place on June 13(shaded area). Source: QCRI
With next-to-nothing time-scales (events and reactions to events happening in minutes) and reach not limited by geography, turmoil may appear anywhere, at any time. One cannot pursue anymore the 'protest front', but can instead focus on the intertwining of multiple sources of disturbances. Thus we can now tracehow a clash with the police in Rio de Janeiro elicits a strong reaction in social media in Rio Grande do Sul and provokes a street protest.
As protest waves continue to sweep through the world, many questions about them remain unanswered. Complexity science can help us get a closer and more comprehensive look at them and perhaps can gives us the opportunity to observe in real time the rise of a new kind of social unrest that the world has not seen before.
Javier Borge-Holthoefer is a member of the Social Computing group at the Qatar Computing Research Institute (QCRI). Founded on interdisciplinary Physics, his research is focused on complex systems ranging from cognitive dynamics to social networks. The views expressed in this article are the author's own and do not necessarily reflect Al Jazeera's editorial policy.
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