Before revision:  We present a novel method to classify relevant tweets during an ongoing crisis without seeing any new examples, using the publicly available dataset of TREC incident streams that provides labeled tweets with 4 relevant classes across 10 different crisis events. Additionally, our method addresses a crucial but missing component from current research in web science for crisis data filtering models: interpretability.
After revision:  We present a novel method to classify relevant tweets during an ongoing crisis without seeing any new examples, using the publicly available dataset of TREC incident streams .