Nowadays, Healthcare Social Networks (HSNs) offer the possibility to enhance patient care and education. However, they also present potential risks for users due to the possible distribution of poor-quality or wrong information along with their bad interpretation. In recent years several discordant information have been diffused in social networks regarding potential risks of flu vaccines. In this paper, by considering a Twitter datasets, we study the accuracy of users' opinions comparing different Machine Learning approaches including Bayesian, Linear and Support Vector Machine (SVM) classifiers.
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