Active Online Learning for Social Media Analysis to Support Crisis Management

People use social media (SM) to describe and discuss different situations they are involved in, like crises. It is therefore worthwhile to exploit SM contents to support crisis management, in particular by revealing useful and unknown information about the crises in real-time. Hence, we propose a novel active online multiple-prototype classifier, called AOMPC. It identifies relevant data related to a crisis. AOMPC is an online learning algorithm that operates on data streams and which is equipped with active learning mechanisms to actively query the label of ambiguous unlabeled data. The number of queries is controlled by a fixed budget strategy. Typically, AOMPC accommodates partly labeled data streams. AOMPC was evaluated using two types of data: (1) synthetic data and (2) SM data from Twitter related to two crises, Colorado Floods and Australia Bushfires. To provide a thorough evaluation, a whole set of known metrics was used to study the quality of the results. Moreover, a sensitivity analysis was conducted to show the effect of AOMPC's parameters on the accuracy of the results. A comparative study of AOMPC against other available online learning algorithms was performed. The experiments showed very good behavior of AOMPC for dealing with evolving, partly-labeled data streams.