Predicting Influenza Outbreak using Constrained Static and dynamic Feature

Twitter is a free social networking and micro-blogging service that gives the opportunity to write and read each others tweet to its 330 million users all over the world with a limitation of 280 characters in each tweet. As a result, Twitter can provide a huge amount of data regarding what is happening at a particular time in all over the world. One of those is epidemic event detection and prediction from the twitter data. In this study, the use of Twitter data to detect influenza outbreak is examined. The result from this experiment shows that estimate of influenza outbreak can be derived from twitter correctly combining constrained supervised and unsupervised features and then using a prediction model.