A framework for detecting public health trends with Twitter

  • Authors:

  • Jon Parker;Yifang Wei;Andrew Yates;Ophir Frieder;Nazli Goharian

  • Affiliations:

  • Georgetown University, Washington, DC and Johns Hopkins University, Baltimore;Georgetown University, Washington, DC;Georgetown University, Washington, DC;Georgetown University, Washington, DC;Georgetown University, Washington, DC

  • Venue:
  • Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
  • Year:
  • 2013

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Abstract

Traditional public health surveillance requires regular clinical reports and considerable effort by health professionals to analyze data. Therefore, a low cost alternative is of great practical use. As a platform used by over 500 million users worldwide to publish their ideas about many topics, including health conditions, Twitter provides researchers the freshest source of public health conditions on a global scale. We propose a framework for tracking public health condition trends via Twitter. The basic idea is to use frequent term sets from highly purified health-related tweets as queries into a Wikipedia article index -- treating the retrieval of medically-related articles as an indicator of a health-related condition. By observing fluctuations in frequent term sets and in turn medically-related articles over a series of time slices of tweets, we detect shifts in public health conditions and concerns over time. Compared to existing approaches, our framework provides a general a priori identification of emerging public health conditions rather than a specific illness (e.g., influenza) as is commonly done.