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

1. Chris Orwa
@blackorwa

2. Building Smart Filters for Election
Crowdsourcing
www.ihub.co.ke/research
@ihubresearch

5. Image courtesy of jtoy.net

7. CASE STUDY:
Assessing the Viability of Crowdsourcing During
Elections in Kenya March 2013

9. Machine Learning

11. Methodology
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Broad keyword filters
Sampling the data
Annotating tweets
Build a classifier
Iterate the process to improve accuracy

15. Advantages
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Obtain unique incidence during an election
Enable comparative analysis
Imperative to first responders
Solves the problem of information
overload

17. Information Dense Environments

20. Digital humanitarianism now has additional
information in its knowledge vault
www.ihub.co.ke/research
@ihubresearch
data@ihub.co.ke