  Whenever a social media user decides to share a story, she is typically
pleased to receive likes, comments, shares, or, more generally, feedback from
her followers. As a result, she may feel compelled to use the feedback she
receives to (re-)estimate her followers' preferences and decides which stories
to share next to receive more (positive) feedback. Under which conditions can
she succeed? In this work, we first look into this problem from a theoretical
perspective and then provide a set of practical algorithms to identify and
characterize such behavior in social media. More specifically, we address the
above problem from the viewpoint of sequential decision making and utility
maximization. For a wide variety of utility functions, we first show that, to
succeed, a user needs to actively trade off exploitation-- sharing stories
which lead to more (positive) feedback--and exploration-- sharing stories to
learn about her followers' preferences. However, exploration is not necessary
if a user utilizes the feedback her followers provide to other users in
addition to the feedback she receives. Then, we develop a utility estimation
framework for observation data, which relies on statistical hypothesis testing
to determine whether a user utilizes the feedback she receives from each of her
followers to decide what to post next. Experiments on synthetic data illustrate
our theoretical findings and show that our estimation framework is able to
accurately recover users' underlying utility functions. Experiments on several
real datasets gathered from Twitter and Reddit reveal that up to 82% (43%) of
the Twitter (Reddit) users in our datasets do use the feedback they receive to
decide what to post next.
