  Recent studies have shown strong correlation between social networking data
and national influenza rates. We expanded upon this success to develop an
automated text mining system that classifies Twitter messages in real time into
six syndromic categories based on key terms from a public health ontology.
10-fold cross validation tests were used to compare Naive Bayes (NB) and
Support Vector Machine (SVM) models on a corpus of 7431 Twitter messages. SVM
performed better than NB on 4 out of 6 syndromes. The best performing
classifiers showed moderately strong F1 scores: respiratory = 86.2 (NB);
gastrointestinal = 85.4 (SVM polynomial kernel degree 2); neurological = 88.6
(SVM polynomial kernel degree 1); rash = 86.0 (SVM polynomial kernel degree 1);
constitutional = 89.3 (SVM polynomial kernel degree 1); hemorrhagic = 89.9
(NB). The resulting classifiers were deployed together with an EARS C2
aberration detection algorithm in an experimental online system.
