We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
OMG U got flu? Analysis of shared health messages for bio-surveillance.
- Authors
Collier, Nigel; Son, Nguyen Truong; Nguyen, Ngoc Mai
- Abstract
Background: Micro-blogging services such as Twitter offer the potential to crowdsource epidemics in real-time. However, Twitter posts ('tweets') are often ambiguous and reactive to media trends. In order to ground user messages in epidemic response we focused on tracking reports of self-protective behaviour such as avoiding public gatherings or increased sanitation as the basis for further risk analysis. Results: We created guidelines for tagging self protective behaviour based on Jones and Salathé (2009)'s behaviour response survey. Applying the guidelines to a corpus of 5283 Twitter messages related to influenza like illness showed a high level of inter-annotator agreement (kappa 0.86). We employed supervised learning using unigrams, bigrams and regular expressions as features with two supervised classifiers (SVM and Naive Bayes) to classify tweets into 4 self-reported protective behaviour categories plus a self-reported diagnosis. In addition to classification performance we report moderately strong Spearman's Rho correlation by comparing classifier output against WHO/NREVSS laboratory data for A(H1N1) in the USA during the 2009-2010 influenza season. Conclusions: The study adds to evidence supporting a high degree of correlation between pre-diagnostic social media signals and diagnostic influenza case data, pointing the way towards low cost sensor networks. We believe that the signals we have modelled may be applicable to a wide range of diseases.
- Subjects
MICROBLOGS; EPIDEMICS; SWINE influenza; VIRUS diseases; COMMUNICABLE diseases
- Publication
Journal of Biomedical Semantics, 2011, Vol 2, p1
- ISSN
2041-1480
- Publication type
Article
- DOI
10.1186/2041-1480-2-S5-S9