Implicit None

José Luis Iribarren and Esteban Moro

Physical Review E 84, 046116 (2011)  [pdf]

Abstract
Despite its importance for rumors or innovations propagation, peer-to-peer collaboration, social networking, or marketing, the dynamics of information spreading is not well understood. Since the diffusion depends on the heterogeneous patterns of human behavior and is driven by the participants’ decisions, its propagation dynamics shows surprising properties not explained by traditional epidemic or contagion models. Here we present a detailed analysis of our study of real viral marketing campaigns where tracking the propagation of a controlled message allowed us to analyze the structure and dynamics of a diffusion graph involving over 31 000 individuals. We found that information spreading displays a non-Markovian branching dynamics that can be modeled by a two-step Bellman-Harris branching process that generalizes the static models known in the literature and incorporates the high variability of human behavior. It explains accurately all the features of information propagation under the “tipping point” and can be used for prediction and management of viral information spreading processes.

P. A. Grabowicz, J. J. Ramasco, E. Moro, J. P. Pujol and V. M. Eguiluz
PLoS ONE 7(1): e29358 (2012)  [text | pdf]

Abstract
An increasing fraction of today’s social interactions occur using online social media as communication channels. Recent worldwide events, such as social movements in Spain or revolts in the Middle East, highlight their capacity to boost people’s coordination. Online networks display in general a rich internal structure where users can choose among different types and intensity of interactions. Despite this, there are still open questions regarding the social value of online interactions. For example, the existence of users with millions of online friends sheds doubts on the relevance of these relations. In this work, we focus on Twitter, one of the most popular online social networks, and find that the network formed by the basic type of connections is organized in groups. The activity of the users conforms to the landscape determined by such groups. Furthermore, Twitter’s distinction between different types of interactions allows us to establish a parallelism between online and offline social networks: personal interactions are more likely to occur on internal links to the groups (the weakness of strong ties); events transmitting new information go preferentially through links connecting different groups (the strength of weak ties) or even more through links connecting to users belonging to several groups that act as brokers (the strength of intermediary ties).

M. Luz Congosto, Montse Fernández y Esteban Moro Egido
Cuadernos de Comunicación Evoca 4 (2011) [pdf]

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We (together with Kimmo Kaski, Aalto University) are organizing the ECCS’11 Satellite conference “Complex Dynamics of Human Interactions” to be held at Vienna, September 14th.

You can find more info at http://www.complexdynamics.org

“It’s not enough to have a map of the structure. It is crucial to understand the dynamics of a process”, L. Barábasi

Scope
The nature of human interaction has undergone a substantial change in the past years and the change does not seem to be over. Technologies like email, smart-phones, social networks like Facebook or broadcast technologies like Twitter transform the way people keep in touch and new trends of communication appear: individuals are continuously connected with each other, social activities are commonly shared by groups of people and people do not need to be geographically close to stay connected.

The high availability of digital data about human activity given by these communication channels and their high detail has provided unprecedented understanding of the nature of humans interactions, that affect the very definition of social relationships, hubs, communities and their role on society. Particular important is the role that human dynamics has in processes that happen concurrently with the dynamics of interaction, like information/disease epidemics in social networks, opinion dynamics, coordination, etc.

The aim of this meeting is to explore the dynamical structure of social and communication networks and the role of the human complex dynamics in realistic processes like information spreading, personal recommendation or “word-of-mouth”, etc.

Specific topics of interest are (but not only):

• High Frequency analysis of communication and social networks
• Causality and correlation in human communication patterns
• Reality Mining, Face-to-Face interactions
• Modeling dynamics of human interactions
• Applications to viral marketing, infection spreading, opinion dynamics.

José Luis Iribarren and Esteban Moro
Social Networks 33, 134-142 (2011)  [pdf]

Abstract
Widespread interest in the diffusion of information through social networks has produced a large number of Social Dynamics models. A majority of them use theoretical hypothesis to explain their diffusion mechanisms while the few empirically based ones average out their measures over many messages of different contents. Our empirical research tracking the step-by-step email propagation of an invariable viral marketing message delves into the content impact and has discovered new and striking features. The topology and dynamics of the propagation cascades display patterns not inherited from the email networks carrying the message. Their disconnected, low transitivity, tree-like cascades present positive correlation between their nodes probability to forward the message and the average number of neighbors they target and show increased participants’ involvement as the propagation paths length grows. Such patterns not described before, nor replicated by any of the existing models of information diffusion, can be explained if participants make their pass-along decisions based uniquely on local knowledge of their network neighbors affinity with the message content. We prove the plausibility of such mechanism through a stylized, agent-based model that replicates the Affinity Paths observed in real information diffusion cascades.

Giovanna Miritello, Esteban Moro y Rubén Lara
Physical Review E (Rapid Comm) 83, 045102 (2011)  [pdf]

Abstract
We investigate the temporal patterns of human communication and its influence on the spreading of information in social networks. The analysis of mobile phone calls of 20 million people in one country shows that human communication is bursty and happens in group conversations. These features have opposite effects in information reach: while bursts hinder propagation at large scales, conversations favor local rapid cascades. To explain these phenomena we define the dynamical strength of social ties, a quantity that encompasses both the topological and temporal patterns of human communication.

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Each day trillions of emails, phone calls, comments on blogs, twitter messages, exchanges in online social networks, etc. are done. Not only the number of communications has increased, but also each of these transactions leaves a digital trace that can be recorded to reconstruct our high-frequency human activity. It is not only the amount and variety of data that is recorded what is important. Also its high-frequency character and its comprehensive nature have allowed researchers, companies and agencies to investigate individual and group dynamics at an unprecedented level of detail and applied them to client modeling, organizational analysis or epidemic spreading [1].

However, for technical or privacy reasons only the existence but not of the content of those exchanges is known. Thus we can quantify the intensity and frequency of the interaction but not its type. For decades, social science has measured relationships between individuals in the currency of tie strength, introduced by Granovetter [1]. Weak ties (loose acquaintances) can help to disseminate ideas and/or innovations between different groups, help to find a job or new information; while strong ties (family, trusted friends) hold together organizations and social groups and can affect emotional health. Despite its success to explain these phenomena, tie strength of human relationships is vaguely defined in most large-scale social empirical work. Specifically, relationships are generally quantified by the intensity or duration of communication, although they are known to have significant drawbacks as tie strength predictor [3,4]. Multiplexity, rhythm and depth of the communication seem to be better predictors of tie strength than intensity [4]. Incorporating those metrics in the data mining of online communication might improve the definition of relationships between individuals and in turn transform our understanding of individual dynamics and its impact in our lives, organizations and society [5]. The challenge is to unveil social relationships in social media and not just mere interactions between individuals, which in general over-represent the real structure of a social group [6] (see figure). And this is of paramount importance to understand the propagation of ideas, opinions, commercial messages, etc. in social networks, since most links declared in social networks might be meaningless from a relationship point of view.

Undressing the social network: considering all e-mail interactions in a academic social network (left) yields to a highly dense and connected social network, while strong interactions (based on the individual relative frequency of communication) render the social group sparser and disconnected

References

1. D. Lazer et al. Computational Social Science, Science 323, 721 (2009)
2. M. S. Granovetter, The Strength of Weak Ties, The American Journal of Sociology 78(6), 1360 (1973)
3. P. V. Marsden, and K. E. Campbell Measuring Tie Strength Social Forces 63(2), 482 (1990).
4. E. Gilbert and K. Karahalios, Predicting Tie Strength with Social Media, presented in CHI 2009.
5. C. T. Butts, Revisting the Foundations of Network Analysis, Science 325, 414 (2009)
6. B. A. Huberman, D. M. Romero, and F. Wu, Social networks that matter, First Monday 14(1) (2009).

Note: This article appears in the Catalog of the exhibition “Culturas del Cambio: Átomos Sociales y Vidas Electrónicas” in the Center Arts Santa Mónica. Thanks to  Josep Perelló for his kind invitation to contribute

La crisis y su efecto en los presupuestos del año 2010 han servido para poner a prueba el compromiso del gobierno de cambiar de modelo productivo y aumentar el gasto en I+D+i. En especial, sufren recortes los gastos del ministerio de Ciencia e Innovación (hasta un 17%), el capítulo 7 (las subvenciones a investigadores) un 17% y los presupuestos de algunos OPIs dependientes del Ministerio con un 15% menos de media. La situación es tal que hasta los propios ministros del gobierno creen que la situación, de continuar, es cuando menos preocupante.

Pero la ciencia no necesita estas tijeras presupuestarias. La principal razón es que la ciencia en este país y en general el I+D+i es un tejido que todavía no está maduro. Llevamos creciendo a buen ritmo en los últimos años y no lo estamos haciendo mal, pero la inversión y los planes en ciencia deben de hacerse a largo plazo, en especial en la contratación de personas. Los que llevamos algún tiempo en esto sabemos que el dinero disponible no siempre es el mismo y podemos ajustarnos el cinturón como antes lo habremos hecho muchos de nosotros, pero como dice Margarita Salas, quienes van a sufrir más este recorte de prespuesto es la gente joven. En definitiva, el futuro de la I+D+i. La gente que tiene que llegar y superarnos y hacerlo mucho mejor que nosotros. Si ya costaba convencer a alguien de que hiciera ciencia en este país, ¿cómo vamos a hacerlo ahora cuando leen en los periódicos que no hay dinero?

Finalmente si el Gobierno y los partidos políticos han estado de acuerdo en hacer un Plan E de miles de millones de euros que nos ha llenado de bonitos carteles nuestra geografía y han mejorado tanto las rotondas de este país, ¿cómo es posible que no se pueda llegar a un acuerdo para crear un Plan I que garantize el crecimiento en I+D+i para llegar a los niveles europeos?

Esta es mi contribución a la iniciativa “La Ciencia Española NO Necesita Tijeras”, promovida por La Aldea Irreductible

We have just published an experimental/theoretical work on the speed of information diffusion in social networks in Physical Review Letters. Specifically we have studied the impact of the heterogeneity of human activity in propagation of emails, rumors, hoaxes, etc. Tracking email marketing campaigns, executed by IBM Corporation in 11 European countries, we were able to compare their viral propagation with our theory (see below the campaigns details).

The results are very simple. Let me give you an example: the typical time between two emails sent by the same person is around 1 day. Traditional models of information diffusion will then yield to an infection speed of 1 day. However, some email computer viruses spread widely in a matter of hours (minutes, sometimes), while some viral propagation (for example the Veuve-Clicquot hoax) last for years. How can that occur? The reason is that traditional models are not correct because they neglect the large heterogeneity in the frequency of human activity: the average time between emails (1 day) does not actually represent the collectivity. In fact, most of us respond very quickly to emails, but some take a lot of time to do it. This fact (known and discovered previously by others) has a profound consequence in the way information spreads:

1. When information spreads “successfully”, in the sense that it propagates and reaches most of the collectivity (i.e. it surpasses the tipping-point), its propagation speed of is determined by the people that have higher activity.
2. However, when information reaches just a small fraction of the population (below the tipping-point), its propagation is controlled by those who take a lot of time to respond/forward and the spreading is very slow.

This phenomenon, as explained in our paper, has consequences for viral marketing, fads and hoaxes diffusion or opinion dynamics because the speed of their messages propagation depends strongly on the size of the sub-communities of very active and not-so active people. For example, in our campaigns (which were below the tipping-point yet successful from a viral marketing perspective), endogenous propagation of the commercial message lasted for months while the average time between getting the message and forwarding was only 1 day. We also found that messages do not “go viral”: They are viral because of the diffusion mechanism they use, but their spreading success largely depends on the social network propensity and heterogeneous behavior.

Finally, our work has some consequences for the way we model and understand human dynamics, since it shows that there is no such a thing as a typical time scale in the human dynamics. This is in sharp contrast with epidemic models, information diffusion models, etc. in which the heterogeneity in human activity and frequency is usually neglected, in favor of a more homogeneous picture of the activity of humans.

Press coverage:

Esteban Moro, Javier Vicente, Luis G. Moyano, Austin Gerig, J. Doyne Farmer, Gabriella Vaglica, Fabrizio Lillo and Rosario N. Mantegna
Submitted to PRE (2009) [pdf]

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