TY - JOUR T1 - Predicting author blog channels with high value future posts for monitoring JF - Proceedings of the Twenty-Fifth Conference on Artificial Intelligence (AAAI) Y1 - 2011 A1 - Wu,S. A1 - Elsayed,T. A1 - Rand, William A1 - Raschid, Louiqa AB - 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 (naıve) 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. ER -