ICCM 2013: Chris Orwa, iHub Research: Deployment of Machine Learning During the Kenyan Election

Social networks are awash with information. Relief agencies such the Kenyan chapter of the Red Cross are already leveraging information from Twitter to track and respond to emergencies. The innumerable amount of information generated within a crisis requires faster processing to extract actionable information. At iHub Research, we studied the flow of information on social media during the March 2013 Kenyan general election and developed a framework looking at the '3Vs of crowdsourcing,' a functional approach to validating, verifying and checking viability of crowdsourced information. As part of the research, machine learning techniques were deployed to sift through 2.6 million tweets and remove non-pertinent data, which narrowed down to 12,000 useful tweets.