  Social media data has emerged as a useful source of timely information about
real-world crisis events. One of the main tasks related to the use of social
media for disaster management is the automatic identification of crisis-related
messages. Most of the studies on this topic have focused on the analysis of
data for a particular type of event in a specific language. This limits the
possibility of generalizing existing approaches because models cannot be
directly applied to new types of events or other languages. In this work, we
study the task of automatically classifying messages that are related to crisis
events by leveraging cross-language and cross-domain labeled data. Our goal is
to make use of labeled data from high-resource languages to classify messages
from other (low-resource) languages and/or of new (previously unseen) types of
crisis situations. For our study we consolidated from the literature a large
unified dataset containing multiple crisis events and languages. Our empirical
findings show that it is indeed possible to leverage data from crisis events in
English to classify the same type of event in other languages, such as Spanish
and Italian (80.0% F1-score). Furthermore, we achieve good performance for the
cross-domain task (80.0% F1-score) in a cross-lingual setting. Overall, our
work contributes to improving the data scarcity problem that is so important
for multilingual crisis classification. In particular, mitigating cold-start
situations in emergency events, when time is of essence.
