Machine Learning Department
School of Computer Science, Carnegie Mellon University


Finding Patterns in Blog Shapes and Blog Evolution

Mary McGlohon, Jure Leskovec, Christos Faloutsos,
Matthew Hurst*, Natalie Glance*

January 2007


Keywords: Social network analysis, principal component analysis, bursty behavior

Can we cluster blogs into types by considering their typical posting and linking behavior? How do blogs evolve over time? In this work we answer these questions, by providing several sets of blog and post features that can help distinguish between blogs. The first two sets of features focus on the topology of the cascades that the blogs are involved in, and the last set of features focuses on the temporal evolution, using chaotic and fractal ideas. We also propose to use PCA to reduce dimensionality, so that we can visualize the resulting clouds of points.

We run all our proposed tools on the ICWSM dataset. Our findings are that (a) topology features can help us distinguish blogs, like 'humor' versus 'conservative' blogs (b) the temporal activity of blogs is very non-uniform and bursty but (c) surprisingly often, it is self-similar and thus can be compactly characterized by the so-called bias factor (the '80' in a recursive 80-20 distribution).

23 pages

*Nielsen Buzzmetrics, Pittsburgh, PA

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