Tracking information epidemics in blogspace

By Eytan Adar, Lada A. Adamic. 2005

454 citations recorded by the source corpus.

Beyond serving as online diaries, Weblogs have evolved into a complex social structure, one which is in many ways ideal for the study of the propagation of information. As Weblog authors discover and republish information, we are able to use the existing link structure of blogspace to track its flow. Where the path by which it spreads is ambiguous, we utilize a novel inference scheme that takes advantage of data describing historical, repeating patterns of "infection." Our paper describes this technique as well as a visualization system that allows for the graphical tracking of information flow.

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

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Citations in the corpus

Uncovering the Temporal Dynamics of Diffusion Networks (2011, background): “Diffusion and propagation processes have received increasing attention in a broad range of domains: information propagation (Adar & Adamic, 2005; Gomez-Rodriguez et al., 2010; Meyers & Leskovec, 2010), social networks (Kempe et al., 2003; Lappas et al., 2010), viral marketing (Watts & Dodds, 2007)…”
Business Intelligence in Blogs: Understanding Consumer Interactions and Communities (2012, background): “Early research on blogs has focused on studying the characteristics of blogs and bloggers, such as the demographics of bloggers (Adar and Adamic 2005; Ali-Hasan and Adamic 2007; Gruhl et al. 2004; Kumar et al. 2005), blogging behavior (Nardi et al. 2004), or the blogging process (Blood 2004).”
The structure of online diffusion networks (2012, background): “2009], or blog networks [Adar and Adamic 2005] and also invoked different metrics, it has previously been dif.cult to draw conclusions about online diffusion processes in general.”
Blogging Practices: An Analytical Framework (2007, background): “Several other studies and experiments have shown that the power law distribution of incoming links exists on various levels of the blogosphere, with consequences for the epidemic-like diffusion of information (Adar & Adamic, 2005; Adar et al., 2004; Wu & Huberman, 2004).”
Prediction of Information Diffusion Probabilities for Independent Cascade Model (2008, background): “Gruhl et al. [9], Adar and Adamic [1], and Leskovec, Adamic and Huberman [12] addressed the problem of tracking the propagation patterns of topics and influence through network spaces.”
Social media and political communication: a social media analytics framework (2012, background): “This concentration also means that a majority of blogs do not link or are not linked by other blogs (Adar and Adamic 2005; Adar et al. 2004; Wu and Huberman 2004).”
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].”
Patterns of Influence in a Recommendation Network (2006, background): “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].”
The Structural Virality of Online Diffusion (2015, background): “Only recently have data satisfying these requirements become available, as a result of online behavior such as blogging (Adar and Adamic 2005), e-commerce (Leskovec et al. 2006), multiplayer gaming (Bakshy et al. 2009), and social networking (Sun et al. 2009, Bakshy et al. 2011, Goel et al. 2012).”
Temporal Networks (2011, background): “Typically, people have studied spreading events in blogs [9,29] or microblogs (like Twitter) [30,31].”
The Matthew effect in empirical data (2014, background): “This is closely related to the workings of the Matthew effect in education (see Section IX), where tests suggest that falling behind in literacy during formative primary school years creates disadvantages that may be difficult to compensate all the way to adulthood [52].”
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].”