  During natural and man-made disasters, people use social media platforms such
as Twitter to post textual and multime- dia content to report updates about
injured or dead people, infrastructure damage, and missing or found people
among other information types. Studies have revealed that this on- line
information, if processed timely and effectively, is ex- tremely useful for
humanitarian organizations to gain situational awareness and plan relief
operations. In addition to the analysis of textual content, recent studies have
shown that imagery content on social media can boost disaster response
significantly. Despite extensive research that mainly focuses on textual
content to extract useful information, limited work has focused on the use of
imagery content or the combination of both content types. One of the reasons is
the lack of labeled imagery data in this domain. Therefore, in this paper, we
aim to tackle this limitation by releasing a large multi-modal dataset
collected from Twitter during different natural disasters. We provide three
types of annotations, which are useful to address a number of crisis response
and management tasks for different humanitarian organizations.
