Millions of tweets, retweets and mentions are exchanged in Twitter everyday about very different subjects, events, opinions, etc. While aggregating this data over a time window might help to understand some properties of those processes in online social networks, the speed of information diffusion around particular time-bound events requires a temporal analysis of them. To show that (and with the help of the Text & Opinion Mining Group at IIC) we collected all tweets (750k) of the vibrant conversation around the disputed subject of the general strike of March 29th in Spain. The data spans 10 days from 03/27 to 04/04 and using the RTs related to the general strike between twitter accounts we build up the following temporal network of information diffusion in Twitter.
Day/night human rhythms are clearly seen, and there is an increase of activity in the evening/night before March 29th, which ended in the burst of RTs during that day. Moreover, using community-finding algorithms over the static (weighted) network of RTs we could assign each twitter account to one of the communities found. Analyzing the text of tweets within those communities we found the nature of the biggest groups: one is in favor of the economic motivations behind the strike, the other is not. Those communities fight close to dominate information propagation in Twitter even some days after the strike.
This video highlights the importance of temporal networks in the analysis of information diffusion in online social networks.
Technical details: the video was done using the amazing igraph package in R and encoded using ffmpeg. Thanks to everyone that contributes to those open-source projects for their work.
Edit (11/9/2012): I have post a tutorial on how to make this kind of visualizations here. Spread the word!
José Luis Iribarren and Esteban Moro
Physical Review E 84, 046116 (2011) [pdf]
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.
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
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]
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]
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.