A Pipeline for Post-Crisis Twitter Data Acquisition
This addresses a practical problem for crisis informatics researchers by enabling efficient data handling from publicly available streams, though it appears incremental as it builds on existing benchmarks and methods.
The paper tackles the problem of rapid acquisition and benchmarking of post-crisis Twitter data by presenting a pipeline for immediate data collection, curation, and relevance filtering, illustrating its utility with a case study on the Las Vegas shootings.
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.