ICCM 2013 : Building Smart Filters for Election Crowdsourcing

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Developing methodology for building smart filters for election based crowdsourcing utilizing machine learning,

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ICCM 2013 : Building Smart Filters for Election Crowdsourcing

  1. 1. Chris Orwa @blackorwa
  2. 2. Building Smart Filters for Election Crowdsourcing www.ihub.co.ke/research @ihubresearch
  3. 3. Image courtesy of jtoy.net
  4. 4. CASE STUDY: Assessing the Viability of Crowdsourcing During Elections in Kenya March 2013
  5. 5. Machine Learning
  6. 6. Methodology • • • • • Broad keyword filters Sampling the data Annotating tweets Build a classifier Iterate the process to improve accuracy
  7. 7. Advantages • • • • Obtain unique incidence during an election Enable comparative analysis Imperative to first responders Solves the problem of information overload
  8. 8. Information Dense Environments
  9. 9. Digital humanitarianism now has additional information in its knowledge vault www.ihub.co.ke/research @ihubresearch data@ihub.co.ke

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