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|Title:||Affinity-driven blog cascade analysis and prediction||Authors:||Li, Hui
Bhowmick, Sourav S.
|Keywords:||DRNTU::Engineering::Computer science and engineering||Issue Date:||2013||Source:||Li, H., Bhowmick, S. S., Sun, A., & Cui, J. (2013). Affinity-driven blog cascade analysis and prediction. Data mining and knowledge discovery.||Series/Report no.:||Data mining and knowledge discovery||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.||URI:||https://hdl.handle.net/10356/98213
|DOI:||10.1007/s10618-013-0307-0||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SCSE Journal Articles|
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