Smarter crowdsourcing for crisis maps

Fellows: Kayla Jacobs, Kwang-Sung Jun, Nathan Leiby
Mentor: Elena Eneva
Project Partner: Ushahidi
[Project Blog Post] [Github Repo]

During natural disasters, social upheavals, and contested elections, it can be hard to know what’s going on. Information becomes scarce due to damaged infrastructure, popular unrest, or a silenced official media.

Tech nonprofit Ushahidi is tackling this problem by harnessing the power of the crowd. They amass field reports from SMS and social media, and map them to give governments, aid agencies, election monitors, and journalists a real-time picture of what’s happening on the ground.

Before a report can be mapped, volunteers must assign a category (e.g., “need food”) and location (“at State St and Lake St”) to it. As the number of reports grows, volunteers are spending too much time doing this manual processing, distracting them from more critical tasks like responding to the messages or vetting their accuracy.

We’re using machine learning to build a smarter review system that learns from volunteers as they categorize reports. Our tool will speed up data processing during emergency situations, reduce volunteer burnout, and empower governments, election monitors, and other responders to spot and address emerging problems more quickly and efficiently.