ICTlogy

Round table: Technopolitics and the 15M. The power of connected crowds. Network system #15M. A new paradigm for distributed politics
Coordinates: Javier Toret

Gestation of the 15M, devices and augmented events, network system, emergence and self-organization. Evolution of the network system.
Javier Toret, Óscar Marín, Alberto Lumbreras, Pablo Aragón, Juan Linares y Miguel Aguilera.  Grupo @datanalysis15M (15Mdata)

Javier Toret: methodological reflections

It is important to stress the different approach of this research: instead of an ex-ante design and hypotheses, the huge amount of data allow for a reverse conception: see what are the patterns that arise and, after them, infer the hypotheses.

The 15M means that a technological and social critical mass takes the street: a long history of movements, unrests and protests finally crystallize as a major protest and camps all over Spain. The profile, though, is not the usual profile of a social movement, but of a network movement: there are several sub-movements in action, several hashtags and memes, several proposals, etc.

A working hypothesis is that as the network movement grows, the interest and participation in “real” politics also grows. Technopolitics is neither slacktivism nor cyberactivism: the goal is real politics and “real life”. Technopolitics is a tactical and strategic use of digital tools and collective identities. The aim of technopolitics is to organize, communicate and act.

Technopolitics have a certain sense of forecasting: they anticipate what is going to happen, or what is about to happen, and help it in finally making it happen, catalysing the change. Technolpolitics drive the flow of the collective action.

Technopolitics, though, heavily rely on technology, in two ways: (1) people intensively use technology to inform and be informed, to coordinate and organize, but also (2) online participation counts as 100% participation, it is not a second best but simply another channel for participation and engagement. Technopolitics normalizes the use of technology.

Another working hypothesis is that the Arab Spring was a reference for the 15M, and the demonstrations on Tahrir Square were key for AcampadaSol (the camps initially in Madrid Puerta del Sol square and after in the rest of Spanish squares).

Alberto Lumbreras: tools

Analysis is done by following the movements of hashtagsin Twitter, including the ‘flocks’ or ‘swarms’ of Twitter users. And thus be able to tell the political relationship between hashtags, users, etc.

To analyse flocks one can either follow the hashtag in real time or recovering data (e.g. from Topsy), analyse what users are following or tweeting two different (but related) hashtags, and then analyze them with a social network analysis software (e.g. Gephi).

This flock analysis allows testing (1) whether the 15M was the product of prior citizen movements in Spain and (2) whether it had any relationship with the Arab Spring. For instance, 31% of the users that tweeted under #spanishrevolution had already twitted #nolesvotes (the movement against bipartidism in Spain). And what also happened is that #spanishrevolution brought back to life #nolesvotes [disclaimer: data are not still very accurate].

Issues: Topsy provides a truncated and thus biased sample of the tweets; we do not know how big has a flock to be to e considered as the generator/influencer of a movement.

Javier Toret: on the precedents of the 15M – Democracia Real Ya – Toma la Calle

Democracia Real Ya was able to activate and engage many existing platforms and groups that had either been very active in citizen protests/demands or were planning to be or wanted to but did not know how (e.g. how to create a critical mass and be relevant).

15M was active in 59 cities through 59 local groups: the explosion of the #15 as a big event/movement turned itself into a massive creation of local camps and local groups connected at a national level, but acting somewhat individually/locally. The affective commotion fostered a distributed and self-organized movement; and the viral propagation was key for the local nodes to be able to be effective.

But, what happened between the 15M until the 22M so that the phenomenon boosted the way it did?

The growth of profiles follows a pattern of simple and logical self-organization fostered by technology. Attention is synced around some very specific issues (e.g. nobody searches “democracy” in Google in Spain… until May 2011, when it peaks!). The 15M can be understood as an event, an augmented event, interconnected and that affects people whether they are present in the physical space or not.

The 15M fosters a cognitive diet: instant messaging, blogging, usage of social networking sites, etc. are intensively used in search of information and communication channels, in search of knowing, in search of understanding.

It is important to note the importance of the subjective/emotional factor of the 15M. The 15M enters the emotions of people and this is shown by what people tweeted those days. There is a need for an emotional analysis of the 15M as it will contribute to explain how it worked and spread.

Óscar Marín: emotions in Twitter

To be able to analyze emotions in Twitter some questions have to be made in order to establish an ontology: what are the predominant emotions in the 15M, what is the emotional charge, what is the relationship between the emotional charge and virality. After these questions, a list of expressions is created that identifies emotions, a grammar is used to detect negations, and a corpus of twitts is tagged manually so that the analysis can be iterated.

What data show is that there is more emotional virality (vs. non-emotional virality) when people are physically together. Or, in other words, the virality of emotional tweets is not unconditionally superior to non-emotional virality, but it depends on people being physically together. We can check this for instance by seeing that emotional charge is much bigger around May 15th 2011 (previous day and a few following days) than on any other date in the time-span of the movement.

Physical events, and the emotions of “empowerment” and “indignation” are they keys to understand the emotional factor in the 15M movement.

Related to emotions, some questions can be also put about the vocabulary: does it evolve, does it have anything to do with virality, what is the frequency (temperature) of a given term, is the vocabulary used coherent, etc.

Data show that, initially, terms rotate with certain speed and that they are weakly related one with each other (low cohesion). As time advances, the vocabulary has much more cohesion, becomes more restricted (less words) and more stable (remain longer in time): the message becomes clearer and stronger. Last, as the core event (camps) fades away, so does the vocabulary, that again has lower cohesion and higher rotation.

Discussion

Q: It would be in interesting outcome of emotional and vocabulary analysis the finding of outliers.

[the session goes on in a second part, which I cannot attend :( ]

Present: Javier Toret, Pablo Aragón and Oscar Marín, members of the group Datanalysis15M.

The research group #datanalysis15m was created to analyse the new movements that emerged in 2011: Arab Spring, Spanish Indignants, Occupy, etc. The main questions being: How can we measure augmented events? How can we measure new ways of organization, of communication, of engagement? How do ideas spread (virally)? How can we characterize network-systems?

About data

In 1969 ARPANET is born as a packet-switching network, which implies a major improvement in communications. With the World Wide Web in 1990, the user can consume information passively online with web browsers and circa 2004 the Web 2.0 is born, where the consumer also becomes a producer. All this activity is increasingly been traced and produces huge amounts of data. This is yet another evolution of the Internet which has been called Big Data.

There are many implications in the generation of such a big amount of data: privacy, security, commoditization of uses and users’ behavior, dematerialization of the economy, information overload, economics of attention, neuromarketing, etc.

Michael Cooley (Architect or Bee? distinguishes between data, information — organized data — knowledge — comprehended and applied information — and wisdom — knowledge put at the service of achieving some specific goals. Wisdom cannot be transmitted and always carries an ethical connotation.

After data acquisition, data analysis is crucial to be able to transform data into information: understanding and structuring data is the core of the information-building process. Last, but still very important, information can be presented in several ways, in what has been called information visualization.

How to organize information:

• Location: maps, dynamic maps and animations.
• Alphabet: lists of words, tag clouds.
• Time: overlapping layers of data along time (to infer correlation or even causality, depending on what comes first in time), timelines (how data evolves along time).
• Category: allows clustering of information and identification of groups.

• Hierarchy: relationships of power/importance between different sets of data.

How technology shapes moods that engage people to act. If we can tell how mood is shaped by technology — or how technology can help in mood-shaping — then technology can help in choosing the appropriate time to invite and engage people to participate.

Engagement is also related to language: the use of the 1st person of plural is much more engaging and viral rather than other alternatives. “We are”, “we can”, etc. has way more punch than “I am” or “they can”.

Network or data laws:

• First law of preferential connection: the more connections a node has, to more likely it is to gain more connections.
• Law of Metcalfe: The value of a network increases proportionally to the square of their users. Behind this law we can find the concept of critical mass: how many propagations have to take place in a network before it explodes.
• The power of histograms: while populations usually follow a normal distribution, histograms usually do not, as people lie in surveys. Thus, adjustments have to be made and caveats must be taken into account.
• Dunbar number: the cognitive number of people is circa 150. After this number, it is very difficult to have quality relationships between people. We can find that in social networking sites, even if people have several hundreds (or thousands) of acquaintances, stable relationships happen in the 100-200 contacts range.
• Power law: big head vs. long tail. Popularity, power, etc. is not evenly distributed, but distributed according to a power law/curve. This complements Pareto’s Law (20% of products represent the 80% of sales): the long tail can get thicker depending on the density of connections in the network, reaching up to 50% of the total of (in this case) sales.
• Zipf’s law: the distribution of the words in a text also follows a power law. 80% of the words in a generic text is irrelevant from a semantic point of view.

Network Analysis

Network Analysis is deeply rooted in Graph Theory models.

Types of social relationships:

• Directed: social relationship is not bi-directional
• Non-directed social relationship is bi-directional
• Explicit: relationships are explicitly stated.
• Implicit: relationships are built through data analysis.

Average distance: number of intermediaries between two different nodes as an average.

Diameter (or effective diameter) of the network is the maximum distance between the most far away nodes. The diameter usually decreases as the network increases (Leskovec, 2007).

Density: proportion of links of a network in relationship with the total of possible links.

Giant component: the biggest connected component in a network. Outside of the giant component, groups are very small.

Clustering coefficient: measures the density of connections between neighbours of a node. Probability of a connection being the connection of another connection. Clusters are linked one to another through weak ties (Granovetter, 1983). Weak ties foster serendipity: weak ties have a higher potential to expose information to their contacts that they would otherwise never discover.

Reciprocity of a directed network measures how many of these relationships are really bi-directional.

Assortativity, associated with haemophilia, is the preference for relationships between users with same or different characteristics. If assortativity (r) is bigger than zero, the network is endogamic; if r < 0, the network is dissortative. Social networking sites tend to be assortative.

Degree distribution: how connections are distributed. Networks free of scale, where a small group of nodes have a high degree of connections and a long tail of nodes with a small number of connections.

Discussion

There are different social networks that are but different layers of the same reality. The purpose of social network analysis is to try to understand one of these layers and how does a specific layer feedbacks with the rest of layers and reality. One can usually find correlations between different networks and how they sync in emotions, contents, bodies.

It is also interesting to state that we increasingly see online behaviours being translated/transposed into “real life”. Not that online networks (their composition) is replicated offline, but that the practices of sharing, communication, decentralization, etc. are also put into practice even without digital technologies, thus reshaping traditionally organized networks.

We also see how information, communications, contents in online networks transcend the platform and permeate in other (offline) media, such as newspapers or TV news. Thus, even if the former network was not significantly representative of reality, the final message does get to a significantly representative share of people.

Another aspect to take into account is, even if the users of a specific social networking site are not representative of the population, whether their behaviour can be a good proxy to predict the general behaviour of the whole population. While this might sound a contradiction (not representativeness leading to predicting the whole population), the key could be in how this sample shapes the agenda of the whole population and thus, in the short or medium term ends up being a good proxy for prediction.