Tracing information flow on a global scale using Internet chain-letter data

By David Liben-Nowell, Jon M. Kleinberg. 2008

412 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics

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In Computer Vision and Pattern Recognition

In Statistical and Nonlinear Physics

Citations in the corpus

The role of social networks in information diffusion (2012, background): “Diffusion and influence have been modeled in blogs [2, 20, 25], email [31], and sites such as Twitter, Digg, and Flickr [8, 21, 29].”
Inferring Networks of Diffusion and Influence (2012, background): “There have been relatively few large-scale studies of information propagation in large networks [Adar and Adamic 2005; Leskovec et al. 2006b, 2007c; Liben-Nowell and Kleinberg 2008].”
Correcting for missing data in information cascades (2011, background): “Diffusion and cascades have been studied in the context of Facebook [25], Twitter [12], Flickr [4], blogs [17], and email chainletters [18].”
Social Clicks: What and Who Gets Read on Twitter? (2016, neutral): “Information diﬀusion naturally shapes collective opinion and consensus [12, 7, 18], hence the role of social media in redistributing inﬂuence online has been under scrutiny ever since blogs and email chains [2, 17].”
Rumor Cascades (2014, background): “Whether studying the spread of news (Bhattacharya and Ram 2012), characterizing the structure of information cascades (Dow, Adamic, and Friggeri 2013; Goel, Watts, and Goldstein 2012; Liben-Nowell and Kleinberg 2008), or estimating influence on the diffusion process (Bakshy et al.”
Quantifying herding effects in crowd wisdom (2014, background): “One active line of enquiry in social network studies is how behavior [1, 2], opinion [7], and information [13, 24, 3] spreads through social networks.”
Searching for superspreaders of information in real-world social media (2014, background): “The network composed of these social links is believed to reliably represent the actual social relations of the LJ users 6 .”
Modeling Information Propagation with Survival Theory (2013, background): “For example, in information propagation, the contagion corresponds to a piece of information (Liben-Nowell & Kleinberg, 2008; Leskovec et al., 2009), the nodes correspond to people and infection events are the times Proceedings of the 30 th International Conference on Machine Learning , Atlanta,…”
How Does the Data Sampling Strategy Impact the Discovery of Information Diffusion in Social Media? (2010, background): “Our experiments reveal that for small sample sizes (30%), a sample that incorporates both topology and usercontext (e.g. location, activity) can improve on naı̈ve methods by a significant margin of ∼15-20%.”
The structure of information pathways in a social communication network (2008, background): “Examples of this include cascades of e-mail recommendations for products [21], cascades of references among bloggers [3, 13, 23], the spread of e-mail chain letters [24], and the search for distant targets in a social network [8, 29].”
Modeling Information Diffusion in Implicit Networks (2010, background): “Even though the diffusion of information has been an active research area recently [7], [12], [26], [28], modeling the diffusion in social networks has proven to be a challenging task.”