title: Machine intelligence for health information: capturing concepts and trends in social media via query expansion.
abstract: BACKGROUND
  We aim to improve retrieval of health information from Twitter.
  
  
  BACKGROUND
  The popularity of social media and micro-blogs has emphasised their potential for knowledge discovery and trend building. However, capturing and relating concepts in these short-spoken and lexically extensive sources of information requires search engines with increasing intelligence.
  
  
  METHODS
  Our approach uses query expansion techniques to associate query terms with the most similar Twitter terms to capture trends in the gamut of information.
  
  
  RESULTS
  We demonstrated the value, defined as improved precision, of our search engine by considering three search tasks and two independent annotators. We also showed the stability of the engine with an increasing number of tweets; this is crucial as large data sets are needed for capturing trends with high confidence. These results encourage us to continue developing the engine for discovering trends in health information available at Twitter.
authors: National ICT Australian, Canberra Research Laboratory, Australia. franksdp32@gmail.com
authors: Xing Yu
authors: XY
authors: Su
authors: Hanna
authors: H
authors: Suominen
authors: Leif
authors: L
authors: Hanlen
mesh_terms: D001185:Artificial Intelligence; D057218:Blogging; D003625:Data Collection / Q000379:methods; D003661:Decision Support Techniques; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008490:Medical Informatics; D061108:Social Media / Q000639:trends*