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Background: Micro-blogging services such as Twitter offer the potential tocrowdsource epidemics in real-time. However, Twitter posts ('tweets') are oftenambiguous and reactive to media trends. In order to ground user messages inepidemic response we focused on tracking reports of self-protective behavioursuch as avoiding public gatherings or increased sanitation as the basis forfurther risk analysis. Results: We created guidelines for tagging selfprotective behaviour based on Jones and Salath\'e (2009)'s behaviour responsesurvey. Applying the guidelines to a corpus of 5283 Twitter messages related toinfluenza like illness showed a high level of inter-annotator agreement (kappa0.86). We employed supervised learning using unigrams, bigrams and regularexpressions as features with two supervised classifiers (SVM and Naive Bayes)to classify tweets into 4 self-reported protective behaviour categories plus aself-reported diagnosis. In addition to classification performance we reportmoderately strong Spearman's Rho correlation by comparing classifier outputagainst WHO/NREVSS laboratory data for A(H1N1) in the USA during the 2009-2010influenza season. Conclusions: The study adds to evidence supporting a highdegree of correlation between pre-diagnostic social media signals anddiagnostic influenza case data, pointing the way towards low cost sensornetworks. We believe that the signals we have modelled may be applicable to awide range of diseases.

OMG U got flu? Analysis of shared health messages for bio-surveillance