Mobilizing Against Austerity? Grab Your Smartphone

Protests against austerity continue to roil parts of European countries, most recently in Brussels earlier this year when 100,000 people took to the streets as well as police deployed water cannons.

This type of public and occasionally violent demonstration has been taking place across Europe since the beginning of the sovereign debt turmoil as leaders decided reducing spending was the best way to deal with the region’s mountain associated with debt. The resulting slashes in benefits and training spending alongside higher taxes and freezes in public field salaries helped galvanize many Europeans onto the streets in protest.

Beyond the immediate impacts associated with austerity and spiking unemployment rates, the other factors shaped the public’utes reaction? In addition, what mobilized individuals to demonstrate in ever-rising numbers throughout Europe?

A new study that I published in the International Journal of Communication helps determine those factors and indicators that they go well beyond the typical suspects of unemployment, revenue and social demographics such as population density.

In fact, my research shows that one of the most basic forms of modern communication and simplest uses of a smartphone, the written text message, was crucial to mobilizing anti-austerity efforts across the European Union. Through a mixture of statistics and network analysis, my study paints an image of what drove the protests and who was behind them.

Predicting protests

The research began with national-level data upon 27 EU member countries (all but Malta). These data looks at the relationships between the prevailing economic conditions over the course of the economic turmoil, from 2007 through 2012.

The analyses started with simple correlations, which found some associations between youth unemployment as well as protest activity that obtained stronger over time, as well as very weak but positive correlations between protests and the quantity of texts sent.

Where it started to get much more interesting, although, was when I controlled for the influence other variables were having on protests, such as democracy levels and population density. This particular happened with regression modeling, which is similar to correlations but allows researchers to account for the influence of other factors, including time, on an outcome. Unlike correlations, regressions can get nearer to (but still not perfectly determine) causal-type relationships.

What I found in the very first regression model was that the volume of text messages sent in all countries was a positive predictor of demonstration activity, as were youth unemployment rates. What this means is that as the total number of text messages went up in one year, demonstration activity increased in the next year. The same was true for youth unemployment rates. More protests followed rising amounts of joblessness among young people.

Combining youngsters unemployment and texting

My statistical analysis showed these relationships weren’t a result of mere chance. The actual regression models took into account a variety of other related factors that perhaps could have also explained the actual increases in protest, for example internet diffusion and income amounts. However, even when taking into account individuals factors, youth unemployment as well as texting levels remained significant.

Even more interesting, though, was that youth joblessness shown this same relationship just in an overall model, and never in year-by-year regressions. That is, it showed up only in figures for the entire series of countries and many years. Of the seven factors modeled, only texting activity remained positive and statistically substantial from 2009 onward within explaining protest movement.

The chart shows the average levels of sociopolitical lack of stability, SMS activity, mobile phone adoption, internet diffusion, and youth unemployment for EU countries through 2007 to 2012. Mobile and internet figures tend to be per 100 people, SMS activity scaling is by Ten million, youth unemployment basis is on rates per 1,000, and sociopolitical instability changes by a factor of One hundred for comparability. Jacob, Writer provided

Therefore, to try to make the most of all these results, I then combined the volume of text messages with the youth unemployment rate to determine the collective impact of these two factors at the same time. Taking a look at what is called an “interaction term” demonstrated that the combination of these two elements had an even stronger relationship to higher levels of protest.

In other words, while rising youth unemployment appeared to be a primary catalyst prompting people to protest, it was the mixture of youth unemployment and the increased frequency of text messaging on mobile phones that was related to demonstrations swelling in both frequency and intensity over this particular six-year time period. The graph at right shows the unity of these factors.

Modeling the communication

What were people communicating through their own mobile devices? Moreover, which mobile phone users were most critical to coordinating these anti-austerity direct orders?

While it wasn’t possible to access the content of all the texts cruising back and forth over this period, for obvious reasons, I was able to examine a lot more than three million public tweets that included relevant key phrases such as “austerity,” “euro” and “crisis” – most of which were sent from mobile devices and all during the last two months of 2012. The tweets serve as a useful stand-in for the content and customers of texts because twitter posts are more visible and consumer names (unlike phone numbers) tend to be publicly accessible.

Using BU’s Twitter Collection and Analysis Tool set (BU-TCAT), I applied several calculations to sort the Twitter data to reveal underlying network structures that shaped the actual flow of information in the network. One of the resulting graphs visualized the most prominent hashtags used, while another identified the most important users discussing the euro crisis on Twitter at the time.

The first of these graphs, the figure below, illustrates the mix and interconnection of subjects related to the euro crisis. In general, this concept map supplies a level of understanding about the conversation taking place in this public room and how conceptual interactions occurred, with out specific coordination or hierarchy, by Twitter users.

This spatialization image shows the relationships of approximately three million tweets concerning the euro crisis in terms of co-occurring hashtags. Jacob Groshek, Author provided

When looking at this graph of co-occuring hashtags, to understand its meaning, imagine if you gave One.5 million people a good orange and asked these to describe its features. Eventually certain dominant descriptive keywords and linkages between words would emerge, such as “juicy,” “round,” “sweet,” “orange” and so on. Now, when looking at this particular co-hashtag graph, imagine that the 1.Five million users that tweeted were not given an orange but rather a chance to publicly express their own opinions on austerity policies. Probably the most prominent nodes in the co-hashtag graph determine which concepts users applied most to describe their views on austerity policies.

A second chart illustrates how there were fairly few connections among the varied stakeholders operating in a network exactly where users were surprisingly sparsely connected with one another. More specifically, what this means is there were more than three zillion tweets in this data set from just over 1.Five million users, but which in the period of these two months, there were only 227 links between the 409 most active users.

To clarify further, this data set was summarized into the leading 409 figures which were tweeting about the euro crisis as well as austerity on Twitter, but there were just 227 instances in which they pointed out one another using the @username reference.

This spatialization shows the connections among about 1.5 million Twitter users, revealing there were only 227 links among 409 of the most energetic users. Jacob Groshek, Author provided

Clearly, there is a large number of tweets and a wide base of users. Nevertheless, this analysis showed that consumer interaction was irregular, usually uncoordinated, and centered around a comparatively small number of users. Additional study identified these users because mostly belonging to a community associated with Spanish journalists, activists and press organizations, along with satirical or pseudo company accounts (such as a deceased politician).

This study signals that these ostensibly open spaces for communication often morph into something else. Rather than as being a connected group of equal participants, the Twitterverse was full of disconnected posts and users where only a few acted as influential gatekeepers to share content.

Smartphones and simple tech rules

Overall, this study found that it was exactly how people used mobile media and not just the pervasiveness of mobile phones (which in itself was not statistically substantial) or online access much more broadly that were pivotal elements in how the public frequently erupted in protest to austerity measures.

In short, the simplest usage of the smartphone proved the key factor – when combined with youngsters unemployment – in explaining where and when people went to the streets to oppose austerity measures.

This is unique and different from how demonstrators in the Arab Spring or any other (reportedly) technologically inspired revolutions have been considered. Whereas individuals protests also engaged cell phones, texting and social media, these were operating in a different cultural and political climate-seeking regime – not policy – change. The results, of course, were also vastly various in that governmental transitions within Europe have been more orderly and programmatic, at a minimum, and that austerity national politics have continued.

Sometimes, the simplest solutions can be the most profound. In this case, the evidence suggests that the public erupting in protest against austerity measures had been driven, above all, by youth unemployment and texting.

In study regarding emerging media and politics, newness is not often nearly as important as “embeddedness” – in this case, the device all of us carry in our pockets, smart phone or not. Simple matters – and, as suggested here, sometimes short (message services) matters too.

How texting helped fuel the anti-austerity protests roiling Europe is actually republished with permission from The Conversation