Structure and dynamics of information pathways in online media

Abstract

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.

A Comprehensive Insight towards Research Direction in Information Propagation

“…Various conceptualized model has also been presented in order to model in better shape. Usage of survival theory was seen in work done by Rodriguez et al [23] in order to develop a new multiplicative model of information propagation. Usage of software component model for constructing architecture towards forecasting the reliability of the message flow was seen in the work of Brosh et al [24].…”

Modeling individual topic-specific behavior and influence backbone networks in social media

“…With regard to influence network structure and authoritative sources discovery, Rodriguez and colleagues [17] were able to infer the structure and dynamics of information (influence) pathways, based on the spread of memes or keywords. Bakshy et al [18] focus on Twitter influencers who are roots of large cascades and have many followers, while Pal et al [19] adopt clustering and ranking based on structural and content characteristics to discover authoritative users.…”

Network topology inference via elastic net structural equation models

“…on maximum likelihood estimation to obtain edge weights [8], [17], [19]. Structural equation models (SEMs) are simple yet capable of capturing causal relationships [11].…”