SwiftRiver sorts realtime social media streams

We are drowning in information, while starving for wisdom. The world henceforth will be run by synthesizers, people able to put together the right information at the right time, think critically about it and make important choices wisely.

E.O. Wilson, Consilience: The Unity of Knowledge

How can we evaluate citizen media for breaking news? Hurricanes? Earthquakes? Contested elections? Crowdsourced crisis information produces a flood of data. Individuals send emails, write blog posts, text friends, post to Twitter. Popular tweets are echoed and amplified. Eyewitness reports may be reliable, or mistaken – or planted disinformation.

SwiftRiver, an initiative of the Ushahidi project, aims to help humans aggregate and evaluate streams of social media. SwiftRiver is an open source platform for managing realtime data streams. Its services can be combined in different ways to serve the needs of crisis responders, journalists, and so on.

The first such app is Sweeper (now in beta), which intelligently filters data feeds for volunteers who then validate and geolocate the information.

Jon Goslin, TED Fellow and SwiftRiver director, talks about the five services underlying the apps.

• Natural language processing to extract meaning.
• Location context: considering how local the source is to reported events.
• Reducing duplicates, especially those from Twitter.
• Accounting for popularity separately from accuracy.
• Reputation management: authority accruing to those for those who have a history of valued posts.