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Social media services such as Twitter are a valuable source of informationfor decision support systems. Many studies have shown that this also holds forthe medical domain, where Twitter is considered a viable tool for public healthofficials to sift through relevant information for the early detection,management, and control of epidemic outbreaks. This is possible due to theinherent capability of social media services to transmit information fasterthan traditional channels. However, the majority of current studies havelimited their scope to the detection of common and seasonal health recurringevents (e.g., Influenza-like Illness), partially due to the noisy nature ofTwitter data, which makes outbreak detection and management very challenging.  Within the European project M-Eco, we developed a Twitter-based EpidemicIntelligence (EI) system, which is designed to also handle a more general classof unexpected and aperiodic outbreaks. In particular, we faced three mainresearch challenges in this endeavor:  1) dynamic classification to manage terminology evolution of Twittermessages, 2) alert generation to produce reliable outbreak alerts analyzing the(noisy) tweet time series, and 3) ranking and recommendation to support domainexperts for better assessment of the generated alerts.  In this paper, we empirically evaluate our proposed approach to thesechallenges using real-world outbreak datasets and a large collection of tweets.We validate our solution with domain experts, describe our experiences, andgive a more realistic view on the benefits and issues of analyzing social mediafor public health.

Why is it Difficult to Detect Sudden and Unexpected Epidemic Outbreaks in Twitter?