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ICCM 2013 : Building Smart Filters for Election Crowdsourcing
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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,

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

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