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Free keywords:
cs.SI,Computer Science, Learning, cs.LG,Statistics, Machine Learning, stat.ML
Abstract:
Many users in online social networks are constantly trying to gain attention
from their followers by broadcasting posts to them. These broadcasters are
likely to gain greater attention if their posts can remain visible for a longer
period of time among their followers' most recent feeds. Then when to post? In
this paper, we study the problem of smart broadcasting using the framework of
temporal point processes, where we model users feeds and posts as discrete
events occurring in continuous time. Based on such continuous-time model, then
choosing a broadcasting strategy for a user becomes a problem of designing the
conditional intensity of her posting events. We derive a novel formula which
links this conditional intensity with the visibility of the user in her
followers' feeds. Furthermore, by exploiting this formula, we develop an
efficient convex optimization framework for the when-to-post problem. Our
method can find broadcasting strategies that reach a desired visibility level
with provable guarantees. We experimented with data gathered from Twitter, and
show that our framework can consistently make broadcasters' post more visible
than alternatives.
