  Millions of people express themselves on public social media, such as
Twitter. Through their posts, these people may reveal themselves as potentially
valuable sources of information. For example, real-time information about an
event might be collected through asking questions of people who tweet about
being at the event location. In this paper, we explore how to model and select
users to target with questions so as to improve answering performance while
managing the load on people who must be asked. We first present a feature-based
model that leverages users exhibited social behavior, including the content of
their tweets and social interactions, to characterize their willingness and
readiness to respond to questions on Twitter. We then use the model to predict
the likelihood for people to answer questions. To support real-world
information collection applications, we present an optimization-based approach
that selects a proper set of strangers to answer questions while achieving a
set of application-dependent objectives, such as achieving a desired number of
answers and minimizing the number of questions to be sent. Our cross-validation
experiments using multiple real-world data sets demonstrate the effectiveness
of our work.
