Date post: | 27-Jun-2015 |
Category: |
Technology |
Upload: | chris-orwa |
View: | 132 times |
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Chris Orwa@blackorwa
Building Smart Filters for Election Crowdsourcing
www.ihub.co.ke/research@ihubresearch
Image courtesy of jtoy.net
CASE STUDY:Assessing the Viability of Crowdsourcing
During Elections in Kenya March 2013
Machine Learning
Methodology
• Broad keyword filters• Sampling the data• Annotating tweets• Build a classifier• Iterate the process to improve
accuracy
Advantages• Obtain unique incidence during an
election• Enable comparative analysis• Imperative to first responders• Solves the problem of information
overload
Information Dense Environments
www.ihub.co.ke/research@ihubresearch
Digital humanitarianism now has additional information in its knowledge vault