A real-time disease surveillance architecture using social networks.

In this paper we proposed surveillance architecture to track diseases-related postings in social networks using Twitter. In each part of the second, the real-time architecture tracks status updates of people as they are posted as soon as possible. Data mining techniques will be used synchronically to crawl, index, extract and classify postings. This work is a part of constructing a global real-time framework for early monitoring diseases outbreaks in social networks.