@article {16435, title = {Predicting author blog channels with high value future posts for monitoring}, journal = {Proceedings of the Twenty-Fifth Conference on Artificial Intelligence (AAAI)}, year = {2011}, month = {2011///}, abstract = {The phenomenal growth of social media, both in scale andimportance, has created a unique opportunity to track infor- mation diffusion and the spread of influence, but can also make efficient tracking difficult. Given data streams rep- resenting blog posts on multiple blog channels and a focal query post on some topic of interest, our objective is to pre- dict which of those channels are most likely to contain a fu- ture post that is relevant, or similar, to the focal query post. We denote this task as the future author prediction problem (FAPP). This problem has applications in information diffu- sion for brand monitoring and blog channel personalization and recommendation. We develop prediction methods in- spired by (na{\i}ve) information retrieval approaches that use historical posts in the blog channel for prediction. We also train a ranking support vector machine (SVM) to solve the problem. We evaluate our methods on an extensive social media dataset; despite the difficulty of the task, all methods perform reasonably well. Results show that ranking SVM prediction can exploit blog channel and diffusion characteris- tics to improve prediction accuracy. Moreover, it is surpris- ingly good for prediction in emerging topics and identifying inconsistent authors. }, author = {Wu,S. and Elsayed,T. and Rand, William and Raschid, Louiqa} }