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
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
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
Simulating the spread of opinions in online social networks when targeting opinion leaders
Information Systems and e-Business Management
Affinity-driven blog cascade analysis and prediction
Data Mining and Knowledge Discovery
| Hi-index | 0.00 |
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 affnity refers to the phenomenon of a blog's inclination to join a specific cascade. We identify and analyze an array of features that may affect a blogger's cascade joining behavior and utilize these features to predict cascade affinity of blogs. Evaluated on a real dataset consisting of 873,496 posts, our svm-based prediction achieved accuracy of 0.723 measured by F1. Our experiments also showed that among all features identified, the number of friends was the most important factor affecting bloggers' inclination to join cascades.