Crowdsourced Crisis Reporting

In crisis situations like contested elections, natural disasters, and troubling humanitarian situations, there's an information gap between the information providers (the voters, disaster survivors, and victims) and the information responders (the election monitors, aid organizations, NGOs and journalists).

Crowdsourced crisis reporting platforms, like Ushahidi and others, aim to narrow this information gap. They provide centralized software to collect, curate, and publish reports coming from the ground.

Here's how the existing report review process works:

1. First, a report is submitted, via SMS, e-mail, Twitter or the web.
2. Next, admins (often volunteers) review the report. They typically perform a number of tasks:

• Identify the language (to see if they have the language skills to process it, and if not route it to a different reviewer) and whether translation is needed.
• Ensure the report hasn't already been submitted, to reduce duplicate work.
• Categorize the message content, figure out the location, and remove any sensitive information like telephone numbers and names.

3. The report is published in some format (e.g., on a map in the Ushahidi system).
4. The publication leads to increased awareness for responders.

Crowdsourced crisis reporting is already a reality, but there's a big problem: currently, the review process is heavily manual. It's slow, tedious, requires domain experts, and doesn't scale up well for large volumes or fast-paced situations.

Our opportunity is to use computing to make this process scale. We've built open-source tools, using natural language processing and machine learning, to support and improve the human review process. No longer must the reviewers do everything from scratch -- now they have automated suggestions to help them. Sensitive information can be automatically flagged, via named entity recognition. Using text classification, categories can be simply confirmed instead of chosen from scratch.

With these tools, crisis report reviewers can reduce the time and tedium they spend processing, and focus their energies on verifying and responding to the reports instead -- the part that really matters.