Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/98213
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dc.contributor.authorLi, Huien
dc.contributor.authorSun, Aixinen
dc.contributor.authorCui, Jiangtaoen
dc.contributor.authorBhowmick, Sourav S.en
dc.date.accessioned2013-11-05T07:42:26Zen
dc.date.accessioned2019-12-06T19:52:07Z-
dc.date.available2013-11-05T07:42:26Zen
dc.date.available2019-12-06T19:52:07Z-
dc.date.copyright2013en
dc.date.issued2013en
dc.identifier.citationLi, H., Bhowmick, S. S., Sun, A., & Cui, J. (2013). Affinity-driven blog cascade analysis and prediction. Data mining and knowledge discovery.en
dc.identifier.urihttps://hdl.handle.net/10356/98213-
dc.description.abstractInformation 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.en
dc.language.isoenen
dc.relation.ispartofseriesData mining and knowledge discoveryen
dc.subjectDRNTU::Engineering::Computer science and engineeringen
dc.titleAffinity-driven blog cascade analysis and predictionen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Engineeringen
dc.identifier.doi10.1007/s10618-013-0307-0en
item.grantfulltextnone-
item.fulltextNo Fulltext-
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