@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}
}
