Twitter - a social media platform - has gained phenomenal popularity among researchers who have explored its massive volumes of data to offer meaningful insights into many aspects of modern life. Twitter has also drawn great interest from public health community to answer many health-related questions regarding the detection and spread of certain diseases. However, despite the growing popularity of Twitter as an influenza detection source among researchers, healthcare officials do not seem to be as intrigued by the opportunities that social media offers for detecting and monitoring diseases. In this paper, we demonstrate that 1) Twitter messages (tweets) can be reliably classified based on influenza related keywords; 2) the spread of influenza can be predicted with high accuracy; and, 3) there is a way to monitor the spread of influenza in selected cities in real-time. We propose an approach to efficiently mine and extract data from Twitter streams, reliably classify tweets based on their sentiment, and visualize data via a real-time interactive map. Our study benefits not only aspiring researchers who are interested in conducting a study involving the analysis of Twitter data but also health sectors officials who are encouraged to incorporate the analysis of vast information from social media data sources, in particular, Twitter.

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Keywords Cold symptoms, Data mining, Flu, Public health surveillance, Sentiment analysis, Social media, Visualization tool
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Conference International Workshop on Software Engineering in Healthcare Systems, SEHS 2016
Byrd, K. (Kenny), Mansurov, A. (Alisher), & Baysal, O. (2016). Mining twitter data for influenza detection and surveillance. Presented at the International Workshop on Software Engineering in Healthcare Systems, SEHS 2016. doi:10.1145/2897683.2897693