Structure and dynamics of information pathways in online media

By Manuel Gomez-Rodriguez, Jure Leskovec, Bernhard Schölkopf. 2012

292 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

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Citations in the corpus

Modeling Information Propagation with Survival Theory (2013, methodology): “…network structure (Gomez-Rodriguez et al., 2010; Snowsill et al., 2011), while others infer not only the network structure but also the strength or the average latency of every edge in the network (Saito et al., 2009; Myers & Leskovec, 2010; Gomez-Rodriguez et al., 2011; 2013; Wang et al., 2012).”
Connecting the Dots: Identifying Network Structure via Graph Signal Processing (2018, unknown): “Related likelihood-based approaches have been proposed to identify traces of network diffusion [46], as well as tensor-based topology identification for dynamic SEMs that can account for (abruptly) switching topological states representing the layers of the graph [58].”
Dynamics of information diffusion and its applications on complex networks (2016, background): “Besides CST-basedmethods, there are some other works [18,261,262] addressing the inverse problem of special topics of complex propagation networks.”
Measuring Emotional Contagion in Social Media (2015, background): “f computational social science [30, 47, 20, 26, 3, 42, 8]. Online social platforms like Facebook and Twitter provide millions of individuals with near-unlimited access to information and connectivity [28, 22, 16]. The content produced on such platforms has proved to impact society at large: from soc”
Information diffusion in online social networks: a survey (2013, background): “[16] extend NETRATE and propose a time-varying inference algorithm, INFOPATH, that uses stochastic gradients to provide on-line estimates of the structure and temporal dynamics of a network that changes over time.”
SNAP: A General Purpose Network Analysis and Graph Mining Library (2016, methodology): “For the case of dynamic networks, in which edges are added or removed over time and we want to infer these dynamic network changes, SNAP provides an alternative algorithm [Gomez-Rodriguez et al. 2013].”