  Mining user-generated data often suffers from the lack of enough labeled
data, short document lengths, and the informal user language. In this paper, we
propose a novel active learning model to overcome these obstacles in the tasks
tailored for query phrases--e.g., detecting positive reports of natural
disasters. Our model has three novelties: 1) It is the first approach to employ
multi-view active learning in this domain. 2) It uses the Parzen-Rosenblatt
window method to integrate the representativeness measure into multi-view
active learning. 3) It employs a query-by-committee strategy, based on the
agreement between predictors, to address the usually noisy language of the
documents in this domain. We evaluate our model in four publicly available
Twitter datasets with distinctly different applications. We also compare our
model with a wide range of baselines including those with multiple classifiers.
The experiments testify that our model is highly consistent and outperforms
existing models.
