A transfer approach to detecting disease reporting events in blog social media

Abstract

Event-Based Epidemic Intelligence (e-EI) has arisen as a body of work which relies upon different forms of pattern recognition in order to detect the disease reporting events from unstructured text that is present on the Web. Current supervised approaches to e-EI suffer both from high initial and high maintenance costs, due to the need to manually label examples to train and update a classifier for detecting disease reporting events in dynamic information sources, such as blogs. In this paper, we propose a new method for the supervised detection of disease reporting events. We tackle the burden of manually labelling data and address the problems associated with building a supervised learner to classify frequently evolving, and variable blog content. We automatically classify outbreak reports to train a supervised learner, and the knowledge acquired from the learning process is then transferred to the task of classifying blogs. Our experiments show that with the automatic classification of training data, and the transfer approach, we achieve an overall precision of 92% and an accuracy of 78.20%.