  This study seeks to validate a search protocol of ill health-related terms
using Twitter data which can later be used to understand if, and how, Twitter
can reveal information on the current health situation. We extracted
conversations related to health and disease postings on Twitter using a set of
pre-defined keywords, assessed the prevalence, frequency, and timing of such
content in these conversations, and validated how this search protocol was able
to detect relevant disease tweets. Classification and Regression Trees (CART)
algorithm was used to train and test search protocols of disease and health
hits comparing to those identified by our team. The accuracy of predictions
showed a good validity with AUC beyond 0.8. Our study shows that monitoring of
public sentiment on Twitter can be used as a real-time proxy for health events.
