Resumen de Affinity-driven blog cascade analysis and prediction
Hui Li, Sourav Bhowmick, Aixin Sun, Jiangtao Cui
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 cascadeis a group of posts linked together discussing about the same topic, and cascade affinityrefers to the phenomenon of a blog’s inclination to join a specific cascade. We identify and analyze an array of macroscopicand microscopiccontent-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-friendsis the most important factor affecting bloggers’ inclination to join cascades.
