Information diffusion through blogspace

By Daniel F. Gruhl, David Liben-Nowell, Ramanathan V. Guha, Andrew Tomkins. 2004

1,303 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

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In Computer Networks and Communications

In Statistical and Nonlinear Physics

Citations in the corpus

Averaging Gone Wrong: Using Time-Aware Analyses to Better Understand Behavior (2016, background): “For example, sociological studies often use students who join a school in the same year to understand the effect of interventions [22, 30], and condition on the year in which people were born to understand people’s behavior, such as variations in financial decision-making [47] or opinions on issues…”
Scalable influence maximization for prevalent viral marketing in large-scale social networks (2010, background): “A few studies have started to address this issue for blogspace [9] and academic collaboration network [20].”
The Pulse of News in Social Media: Forecasting Popularity (2012, background): “Among these are models of viral marketing [7], models of attention on the web [8], cascading behavior in propagation of information [9] [10] and models that describe heavy tails in human dynamics [11].”
Patterns of Cascading Behavior in Large Blog Graphs (2007, methodology): “This may, however, be due in part to the Web-crawling and text analysis techniques used to infer relationships among posts [2, 10].”
Conversations in the Blogosphere: An Analysis "From the Bottom Up" (2005, background): “Adar, Zhang, Adamic, and Lukose (2004) identify blogs that initiate "information epidemics" and visualize the paths specific infections take through blogspace (see also Gruhl, Guha, Liben-Nowell, & Tomkins, 2004 ).”
Tractable Models for Information Diffusion in Social Networks (2006, methodology): “have been studied in various fields including epidemiology, sociology, marketing and physics [5,4], one of the conceptually simplest models is the Independent Cascade Model (ICM) used by Goldenberg et al.”
Learning the graph of epidemic cascades (2012, background): “trending of topics and hashtags on Twitter/Facebook[16], keywords on blog networks[4]) (c) e-commerce: understanding epidemic cascades (and, in this case, finding influential nodes) is crucial to viral marketing [5], and predicting/optimizing uptake on social buying sites like Groupon, LivingSocial ”
Meme-tracking and the dynamics of the news cycle (2009, background): “Some of the key research issues here have been the identification of topics over time [5, 11, 16], the evolving practices of bloggers [25, 26], the cascading adoption of stories [3, 14, 20, 23], and the ideological di-”
The convergence of social and technological networks (2008, background): “2, 14 Large-scale social contagion data also provides the opportunity to identify highly influential sets of people in a social network—the set of people who would trigger the largest cascade if they were to adopt an innovation.”
The predictive power of online chatter (2005, background): “The propagation of information through Blogspace was studied in [14]; here, the authors propose a model for flow of information from person to person in Blogspace.”
What is Twitter, a social network or a news media? (2010, background): “Topic propagation in blogspace [10], linking patterns in blog graph [21], favorite photo marking in a social photo sharing service [4], fanning in Facebook [34], Internet chain letter forwarding [24], and meme tracking in news cycles [20] all report on large-scale information diffusion online.”
Mobile Data Offloading through Opportunistic Communications and Social Participation (2012, background): “Gruhl et al. [24] study the dynamic of information diffusion in blogspace .”
On the Bursty Evolution of Blogspace (2003, methodology): “Later work on blogs covers the duration and structure of blog topics, and the propagation of ideas (or memes) through blogspace using models drawn from the theory of infectious diseases [ 11 ].”
A word at a time: computing word relatedness using temporal semantic analysis (2011, background): “[18] and others [22] analyzed temporal information diffusion in blogosphere, including the temporal patterns in word popularity.”
How Does the Data Sampling Strategy Impact the Discovery of Information Diffusion in Social Media? (2010, methodology): “…ranging from recommendation systems, online advertising, user behavior prediction and disease containment (Kempe, Kleinberg, and Tardos 2003), (Gruhl et al. 2004), (Song et al. 2006), (Anagnostopoulos, Kumar, and Mahdian 2008), (Kossinets, Kleinberg, and Watts 2008), (Liben-Nowell and…”