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Affinity-driven blog cascade analysis and prediction

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Abstract

Information propagation within the blogosphere is of much importance in implementing policies, marketing research, launching new products, and other applications. In this paper, we take a microscopic view of the information propagation pattern in blogosphere by investigating blog cascade affinity. A blog cascade is a group of posts linked together discussing about the same topic, and cascade affinity refers to the phenomenon of a blog’s inclination to join a specific cascade. We identify and analyze an array of macroscopic and microscopic content-oblivious features that may affect a blogger’s cascade joining behavior and utilize these features to predict cascade affinity of blogs. Based on these features, we present two non-probabilistic and probabilistic strategies, namely support vector machine (SVM) classification-based approach and Bipartite Markov Random Field-based (BiMRF) approach, respectively, to predict the probability of blogs’ affinity to a cascade and rank them accordingly. Evaluated on a real dataset consisting of 873,496 posts, our experimental results demonstrate that our prediction strategy can generate high quality results (\(F1\)-measure of 72.5 % for SVM and 71.1 % for BiMRF) comparing with the approaches using traditional or singular features only such as elapsed time, number of participants which is around 11.2 and 8.9 %, respectively. Our experiments also showed that among all features identified, the number of quasi-friends is the most important factor affecting bloggers’ inclination to join cascades.

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Notes

  1. http://technorati.com

  2. A shorter version of this work has been published in (Li et al. 2009).

  3. http://technorati.com/developers/api

  4. We adopted the method described in paper Chekuri et al. (2006) for fitting power-law distributions

    Table 2 Statistics of the data set
    Full size table

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Acknowledgments

Part of the work was done when the first author was pursuing PhD in School of Computer Engineering, Nanyang Technological University, Singapore. This work is partly supported by NSFC 61202179, 61173089

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Authors and Affiliations

  1. School of Computer Science and Technology, Xidian University, Xi’an, China

    Hui Li & Jiangtao Cui

  2. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Sourav S Bhowmick & Aixin Sun

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  1. Hui Li
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  2. Sourav S Bhowmick
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  3. Aixin Sun
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  4. Jiangtao Cui
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Correspondence to Hui Li.

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Responsible editor: Bing Liu.

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Li, H., Bhowmick, S.S., Sun, A. et al. Affinity-driven blog cascade analysis and prediction. Data Min Knowl Disc 28, 442–474 (2014). https://doi.org/10.1007/s10618-013-0307-0

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