Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/98213
Title: Affinity-driven blog cascade analysis and prediction
Authors: Li, Hui
Sun, Aixin
Cui, Jiangtao
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
http://hdl.handle.net/10220/17314
DOI: 10.1007/s10618-013-0307-0
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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