On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
On the bursty evolution of blogspace
WWW '03 Proceedings of the 12th international conference on World Wide Web
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Information diffusion through blogspace
Proceedings of the 13th international conference on World Wide Web
The dynamics of viral marketing
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
Identifying the influential bloggers in a community
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Discovering information diffusion paths from blogosphere for online advertising
Proceedings of the 1st international workshop on Data mining and audience intelligence for advertising
Optimal marketing strategies over social networks
Proceedings of the 17th international conference on World Wide Web
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining social networks using heat diffusion processes for marketing candidates selection
Proceedings of the 17th ACM conference on Information and knowledge management
Blocking links to minimize contamination spread in a social network
ACM Transactions on Knowledge Discovery from Data (TKDD)
Behavioral profiles for advanced email features
Proceedings of the 18th international conference on World wide web
A measurement-driven analysis of information propagation in the flickr social network
Proceedings of the 18th international conference on World wide web
Discovering the staring people from social networks
Proceedings of the 18th international conference on World wide web
User grouping behavior in online forums
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Predictive Ensemble Pruning by Expectation Propagation
IEEE Transactions on Knowledge and Data Engineering
Blog cascade affinity: analysis and prediction
Proceedings of the 18th ACM conference on Information and knowledge management
A greedy approximation algorithm for the group Steiner problem
Discrete Applied Mathematics
AdHeat: an influence-based diffusion model for propagating hints to match ads
Proceedings of the 19th international conference on World wide web
Using a model of social dynamics to predict popularity of news
Proceedings of the 19th international conference on World wide web
Finding influentials based on the temporal order of information adoption in twitter
Proceedings of the 19th international conference on World wide web
Discovery of latent subcommunities in a blog's readership
ACM Transactions on the Web (TWEB)
Community-based greedy algorithm for mining top-K influential nodes in mobile social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
The task-dependent effect of tags and ratings on social media access
ACM Transactions on Information Systems (TOIS)
Identifying topical authorities in microblogs
Proceedings of the fourth ACM international conference on Web search and data mining
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A data-based approach to social influence maximization
Proceedings of the VLDB Endowment
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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.