Information diffusion through blogspace

Abstract

We study the dynamics of information propagation in environments of low-overhead personal publishing, using a large collection of weblogs over time as our example domain. We characterize and model this collection at two levels. First, we present a macroscopic characterization of topic propagation through our corpus, formalizing the notion of long-running "chatter" topics consisting recursively of "spike" topics generated by outside world events, or more rarely, by resonances within the community. Second, we present a microscopic characterization of propagation from individual to individual, drawing on the theory of infectious diseases to model the flow. We propose, validate, and employ an algorithm to induce the underlying propagation network from a sequence of posts, and report on the results.

Patterns of Influence in a Recommendation Network

“…Most work on extracting cascades from large-scale on-line data has been done in the blog domain [1,7,10]. The authors in this domain note that, while information propagates between blogs, examples of genuine cascading behavior appeared relatively rare.…”

“…Associated with each recommendation is the product involved, and the time the recommendation was made. Studies of blogspace either spend a lot of effort mining topics from posts [2,7] or consider only the properties of blogspace as a graph of unlabeled URLs [1,10]. Temporally evolving graphs are explored in [4].…”

“…Cascades are also known as "fads" or "resonance." Cascades have been studied for many years by sociologists concerned with the diffusion of innovation [15]; more recently, researchers in several fields have investigated cascades for the purpose of selecting trendsetters for viral marketing [9,14], finding inoculation targets in epidemiology [13], and explaining trends in blogspace [2,7,10]. Despite much empirical work in the social sciences on datasets of moderate size, the difficulty in obtaining data has limited the extent of analysis on very large-scale, complete datasets representing cascades.…”

Finding Critical Nodes for Inhibiting Diffusion of Complex Contagions in Social Networks

“…[31,9,20,21,7,32]). With respect to diffusion in social networks, researchers have studied the propagation of favorite photographs in a Flickr network [6], the spread of information [16,23] via Internet communication, and the effects of online purchase recommendations [26], to name a few. In some instances, models of diffusion are combined with data mining to predict social phenomena (e.g., product marketing [9,31] and trust propagation [17]).…”

How are topics born? Understanding the research dynamics preceding the emergence of new areas

“…Topic detection and tracking is a task that has drawn much attention in recent years and has been applied to a variety of scenarios, such as social networks (Cataldi, Di Caro & Schifanella, 2010;Mathioudakis & Koudas, 2010), blogs (Gruhl et al, 2004;Oka, Abe & Kato, 2006), emails (Morinaga & Yamanishi, 2004 and scientific literature (Bolelli, Ertekin & Giles, 2009;Decker et al, 2007;Erten et al, 2004;Lv et al, 2011;Osborne, Scavo & Motta, 2014;Sun, Ding & Lin, 2016;Tseng et al, 2009).…”