  Due to instant availability of data on social media platforms like Twitter,
and advances in machine learning and data management technology, real-time
crisis informatics has emerged as a prolific research area in the last decade.
Although several benchmarks are now available, especially on portals like
CrisisLex, an important, practical problem that has not been addressed thus far
is the rapid acquisition and benchmarking of data from free, publicly available
streams like the Twitter API. In this paper, we present ongoing work on a
pipeline for facilitating immediate post-crisis data collection, curation and
relevance filtering from the Twitter API. The pipeline is minimally supervised,
alleviating the need for feature engineering by including a judicious mix of
data preprocessing and fast text embeddings, along with an active learning
framework. We illustrate the utility of the pipeline by describing a recent
case study wherein it was used to collect and analyze millions of tweets in the
immediate aftermath of the Las Vegas shootings.
