Discovering Health Beliefs in Twitter

Social networking websites such as Twitter have invigorated a wide range of studies in recent years ranging from consumer opinions on products to tracking the spread of diseases. While sentiment analysis and opinion mining from tweets have been studied extensively, surveillance of beliefs, especially those related to public health, have received considerably less attention. In our previous work, we proposed a model for surveillance of health beliefs on Twitter relying on the use of hand-picked probe statements expressing various health-related propositions. In this work we extend our model to automatically discover various probes related to public health beliefs. We present a data driven approach based on two distinct datasets and study the prevalence of public belief, disbelief or doubt for newly discovered probe statements.