| 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 (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.
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