Ontology driven machine learning approach for disease name extraction from Twitter messages

Twitter and social media as a whole has great potential as a source of disease surveillance data however the general messiness of tweets presents several challenges for standard information extraction methods. Current methods for disease surveillance on twitter rely on inflexible keyword based approaches that require messages to be pre-filtered on the basis of a disease name which is supplied a priori and are not capable of detecting new ailments. In this paper we present an ontology based machine learning approach to extract disease names and expressions describing ailments from tweets which may be employed as part of a larger general purpose system for automated disease incidence monitoring. We also propose a simple methodology for automatic detection and correction of errors.