  Social media generates an enormous amount of data on a daily basis but it is
very challenging to effectively utilize the data without annotating or labeling
it according to the target application. We investigate the problem of localized
flood detection using the social sensing model (Twitter) in order to provide an
efficient, reliable and accurate flood text classification model with minimal
labeled data. This study is important since it can immensely help in providing
the flood-related updates and notifications to the city officials for emergency
decision making, rescue operations, and early warnings, etc. We propose to
perform the text classification using the inductive transfer learning method
i.e pre-trained language model ULMFiT and fine-tune it in order to effectively
classify the flood-related feeds in any new location. Finally, we show that
using very little new labeled data in the target domain we can successfully
build an efficient and high performing model for flood detection and analysis
with human-generated facts and observations from Twitter.
