  The first objective towards the effective use of microblogging services such
as Twitter for situational awareness during the emerging disasters is discovery
of the disaster-related postings. Given the wide range of possible disasters,
using a pre-selected set of disaster-related keywords for the discovery is
suboptimal. An alternative that we focus on in this work is to train a
classifier using a small set of labeled postings that are becoming available as
a disaster is emerging. Our hypothesis is that utilizing large quantities of
historical microblogs could improve the quality of classification, as compared
to training a classifier only on the labeled data. We propose to use unlabeled
microblogs to cluster words into a limited number of clusters and use the word
clusters as features for classification. To evaluate the proposed
semi-supervised approach, we used Twitter data from 6 different disasters. Our
results indicate that when the number of labeled tweets is 100 or less, the
proposed approach is superior to the standard classification based on the bag
or words feature representation. Our results also reveal that the choice of the
unlabeled corpus, the choice of word clustering algorithm, and the choice of
hyperparameters can have a significant impact on the classification accuracy.
