Ontology Driven Disease Incidence Detection on Twitter
This addresses the problem of generic disease surveillance on social media for public health applications, though it appears incremental as it builds on existing ontological and NLP methods.
The authors tackled automated disease incidence monitoring on Twitter by using an ontology of disease concepts to represent tweets, achieving good performance on classifying mentions of various diseases with models trained on influenza and Listeria data.
In this work we address the issue of generic automated disease incidence monitoring on twitter. We employ an ontology of disease related concepts and use it to obtain a conceptual representation of tweets. Unlike previous key word based systems and topic modeling approaches, our ontological approach allows us to apply more stringent criteria for determining which messages are relevant such as spatial and temporal characteristics whilst giving a stronger guarantee that the resulting models will perform well on new data that may be lexically divergent. We achieve this by training learners on concepts rather than individual words. For training we use a dataset containing mentions of influenza and Listeria and use the learned models to classify datasets containing mentions of an arbitrary selection of other diseases. We show that our ontological approach achieves good performance on this task using a variety of Natural Language Processing Techniques. We also show that word vectors can be learned directly from our concepts to achieve even better results.