  Social media sources can provide crucial information in crisis situations,
but discovering relevant messages is not trivial. Methods have so far focused
on universal detection models for all kinds of crises or for certain crisis
types (e.g. floods). Event-specific models could implement a more focused
search area, but collecting data and training new models for a crisis that is
already in progress is costly and may take too much time for a prompt response.
As a compromise, manually collecting a small amount of example messages is
feasible. Few-shot models can generalize to unseen classes with such a small
handful of examples, and do not need be trained anew for each event. We compare
how few-shot approaches (matching networks and prototypical networks) perform
for this task. Since this is essentially a one-class problem, we also
demonstrate how a modified one-class version of prototypical models can be used
for this application.
