The dynamic of information-driven coordination phenomena: a transfer entropy analysis

TL;DR

This paper introduces a transfer entropy-based method to analyze social media data, revealing how collective social phenomena emerge and transition through changes in information transfer dynamics and influence networks.

Contribution

It develops a novel symbolic transfer entropy framework to detect and characterize the onset and structural transitions of social collective phenomena from micro-blogging data.

Findings

Identifies a change in the time-scale of information transfer at the onset of collective events.

Detects an order-disorder transition in influence networks during social phenomena.

Validates the methodology across five empirical case studies.

Abstract

Data from social media are providing unprecedented opportunities to investigate the processes that rule the dynamics of collective social phenomena. Here, we consider an information theoretical approach to define and measure the temporal and structural signatures typical of collective social events as they arise and gain prominence. We use the symbolic transfer entropy analysis of micro-blogging time series to extract directed networks of influence among geolocalized sub-units in social systems. This methodology captures the emergence of system-level dynamics close to the onset of socially relevant collective phenomena. The framework is validated against a detailed empirical analysis of five case studies. In particular, we identify a change in the characteristic time-scale of the information transfer that flags the onset of information-driven collective phenomena. Furthermore, our approach identifies an order-disorder transition in the directed network of influence between social sub-units. In the absence of a clear exogenous driving, social collective phenomena can be represented as endogenously-driven structural transitions of the information transfer network. This study provides results that can help define models and predictive algorithms for the analysis of societal events based on open source data.