A real-time architecture for detection of diseases using social networks: design, implementation and evaluation

Abstract

In this work we developed a surveillance architecture to detect diseases-related postings in social networks using Twitter as an example for a high-traffic social network. Our real-time architecture uses Twitter streaming API to crawl Twitter messages as they are posted. Data mining techniques have been used to index, extract and classify postings. Finally, we evaluate the performance of the classifier with a dataset of public health postings and also evaluate the run-time performance of whole system with respect to latency and throughput.