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.

本研究提出了一种新的主动学习模型，利用多视图主动学习、Parzen-Rosenblatt 窗口方法和基于预测器的委员会策略等，用于检测自然灾害的正向报道，并在四个不同应用的 Twitter 数据集上通过实验验证该模型的高一致性和优越性。