Structure and Dynamics of Information Pathways in Online Media

Diffusion of information, spread of rumors and infectious diseases are all instances of stochastic processes that occur over the edges of an underlying network. Many times networks over which contagions spread are unobserved, and such networks are often dynamic and change over time. In this paper, we investigate the problem of inferring dynamic networks based on information diffusion data. We assume there is an unobserved dynamic network that changes over time, while we observe the results of a dynamic process spreading over the edges of the network. The task then is to infer the edges and the dynamics of the underlying network. We develop an on-line algorithm that relies on stochastic convex optimization to efficiently solve the dynamic network inference problem. We apply our algorithm to information diffusion among 3.3 million mainstream media and blog sites and experiment with more than 179 million different pieces of information spreading over the network in a one year period. We study the evolution of information pathways in the online media space and find interesting insights. Information pathways for general recurrent topics are more stable across time than for on-going news events. Clusters of news media sites and blogs often emerge and vanish in matter of days for on-going news events. Major social movements and events involving civil population, such as the Libyan's civil war or Syria's uprise, lead to an increased amount of information pathways among blogs as well as in the overall increase in the network centrality of blogs and social media sites.Comment: To Appear at the 6th International Conference on Web Search and Data Mining (WSDM '13

Evolutionary Stability of Ideal Free Dispersal under Spatial Heterogeneity and Time Periodicity

Roughly speaking, a population is said to have an ideal free distribution on a spatial region if all of its members can and do locate themselves in a way that optimizes their fitness, allowing for the effects of crowding. Dispersal strategies that can lead to ideal free distributions of populations using them have been shown to exist and to be evolutionarily stable in a number of modeling contexts in the case of habitats that vary in space but not in time. Those modeling contexts include reaction-diffusion-advection models and the analogous models using discrete diffusion or nonlocal dispersal described by integro-differential equations. Furthermore, in the case of reaction-diffusion-advection models and their nonlocal analogues, there are strategies that allow populations to achieve an ideal free distribution by using only local information about environmental quality and/or gradients. We show that in the context of reaction-diffusion-advection models for time-periodic environments with spatially varying resource levels, where the total level of resources in an environment remains fixed but its location varies seasonally, there are strategies that allow populations to achieve an ideal free distribution. We also show that those strategies are evolutionarily stable. However, achieving an ideal free distribution in a time-periodic environment requires the use of nonlocal information about the environment such as might be derived from experience and memory, social learning, or genetic programming. This is joint work with Chris Cosner

What Influences the Diffusion of Grassroots Innovations for Sustainability? Investigating Community Currency Niches

Community action for sustainability is a promising site of socio-technical innovation. Here we test the applicability of co-evolutionary niche theories of innovation diffusion (Strategic Niche Management, SNM) to the context of ‘grassroots innovations’. We present new empirical findings from an international study of 12 community currency niches (such as LETS, time banks, local currencies). These are parallel systems of exchange, designed to operate alongside mainstream money, meeting additional sustainability needs. Our findings confirm SNM predictions that niche-level activity correlates with diffusion success, but we highlight additional or confounding factors, and how niche theories might be adapted to better fit civil-society innovations. In so doing, we develop a model of grassroots innovation niche diffusion which builds on existing work and tailors it to this specific context. The paper concludes with a series of theoretically-informed recommendations for practitioners and policymakers to support the development and potential of grassroots innovations