  We present novel active learning strategies dedicated to providing a solution
to the cold start stage, i.e. initializing the classification of a large set of
data with no attached labels. Moreover, proposed strategies are designed to
handle an imbalanced context in which random selection is highly inefficient.
Specifically, our active learning iterations address label scarcity and
imbalance using element scores, combining information extracted from a
clustering structure to a label propagation model. The strategy is illustrated
by a case study on annotating Twitter content w.r.t. testimonies of a real
flood event. We show that our method effectively copes with class imbalance, by
boosting the recall of samples from the minority class.
