  Social media platforms provide active communication channels during mass
convergence and emergency events such as disasters caused by natural hazards.
As a result, first responders, decision makers, and the public can use this
information to gain insight into the situation as it unfolds. In particular,
many social media messages communicated during emergencies convey timely,
actionable information. Processing social media messages to obtain such
information, however, involves solving multiple challenges including: handling
information overload, filtering credible information, and prioritizing
different classes of messages. These challenges can be mapped to classical
information processing operations such as filtering, classifying, ranking,
aggregating, extracting, and summarizing. We survey the state of the art
regarding computational methods to process social media messages, focusing on
their application in emergency response scenarios. We examine the
particularities of this setting, and then methodically examine a series of key
sub-problems ranging from the detection of events to the creation of actionable
and useful summaries.
