  Since the events of the Arab Spring, there has been increased interest in
using social media to anticipate social unrest. While efforts have been made
toward automated unrest prediction, we focus on filtering the vast volume of
tweets to identify tweets relevant to unrest, which can be provided to
downstream users for further analysis. We train a supervised classifier that is
able to label Arabic language tweets as relevant to unrest with high
reliability. We examine the relationship between training data size and
performance and investigate ways to optimize the model building process while
minimizing cost. We also explore how confidence thresholds can be set to
achieve desired levels of performance.
