  This paper complements the large body of social sensing literature by
developing means for augmenting sensing data with inference results that
"fill-in" missing pieces. It specifically explores the synergy between (i)
inference techniques used for filling-in missing pieces and (ii) source
selection techniques used to determine which pieces to retrieve in order to
improve inference results. We focus on prediction in disaster scenarios, where
disruptive trend changes occur. We first discuss our previous conference study
that compared a set of prediction heuristics and developed a hybrid prediction
algorithm. We then enhance the prediction scheme by considering algorithms for
sensor selection that improve inference quality. Our proposed source selection
and extrapolation algorithms are tested using data collected during the New
York City crisis in the aftermath of Hurricane Sandy in November 2012. The
evaluation results show that consistently good predictions are achieved. The
work is notable for addressing the bi-modal nature of damage propagation in
complex systems subjected to stress, where periods of calm are interspersed
with periods of severe change. It is novel in offering a new solution to the
problem that jointly leverages source selection and extrapolation components
thereby improving the results.
