  Social media is often utilized as a lifeline for communication during natural
disasters. Traditionally, natural disaster tweets are filtered from the Twitter
stream using the name of the natural disaster and the filtered tweets are sent
for human annotation. The process of human annotation to create labeled sets
for machine learning models is laborious, time consuming, at times inaccurate,
and more importantly not scalable in terms of size and real-time use. In this
work, we curate a silver standard dataset using weak supervision. In order to
validate its utility, we train machine learning models on the weakly supervised
data to identify three different types of natural disasters i.e earthquakes,
hurricanes and floods. Our results demonstrate that models trained on the
silver standard dataset achieved performance greater than 90% when classifying
a manually curated, gold-standard dataset. To enable reproducible research and
additional downstream utility, we release the silver standard dataset for the
scientific community.
