Content Analysis of Syndromic Twitter Data

How to Cite

Keffala, B., Conway, M., Doan, S., & Collier, N. (2013). Content Analysis of Syndromic Twitter Data. Online Journal of Public Health Informatics, 5(1).


We present the results of a content analysis of tweets related to respiratory syndrome. An annotation scheme was developed to differentiate between true positive and false positive tweets, and to quantify more fine-grained information about the content of the tweets. This annotation scheme is general, and as such can be used to aid in surveillance of different syndromes. In addition to finding good separation between true and false positive tweets, results showed that users referencing respiratory syndrome were more likely to discuss their own, current experience than they were to reference another person's symptoms or symptoms not currently being experienced, that expressed sentiment was largely negative, and that there was significant use of expressions of aspiration or hyperbole.
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