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

data@ihub.co.ke

Digital humanitarianism now has additional information in its knowledge vault