SwiftRiver is an open source platform that helps users manage realtime data.
In the above videos: Patrick talks with Robert Scoble, Jon Gosier talks media curation, Anil Dash at CHIRPIN THE MEDIA
UX Magazine, TED 2009, BBC, Computer World, GigaOM, TED 2010, CHIRP 2010, Scobleizer, Appfricast
ABOUT
SwiftRiver is a free and open source platform that helps people make sense of a lot of information in a short amount of time. The SwiftRiver platform was born out of the need to understand and act upon a wave of massive amounts of crisis data that tends to overwhelm in the first 24 hours of a disaster. Since then, there has been a great deal of interest in this tool for other industries, such as news rooms and brand monitoring groups.
In practice, SwiftRiver enables the filtering and verification of real-time data from channels such as Twitter, SMS, Email and RSS feeds. This free tool is especially useful for organizations who need to sort their data by authority and accuracy, as opposed to popularity. These organizations include the media, emergency response groups, election monitors and more. This might include journalists and other media institutions, emergency response groups, election monitors and more.
How Does it Work?
Although the general concept is simple, Swift relies on three incredibly complex technologies: Natural Language Computation, Machine Learning and Veracity Algorithms. The sum of these parts allows an emergency response organization to track and verify the accuracy of reports during a crisis, or a team of journalists might use Swift to track specific topics they happen to be researching. Swift helps surface authoritative sources, while suppressing noise (like duplicate content, irrelevant cross-chatter and inaccuracies.)
But how? When users begin monitoring a topic, they enter several related feeds. Swift begins aggregating these feeds, taking multiple channels, mashing them together and outputting one unified feed. To clarify, the user may be tracking Twitter, various Blogs, as well as a dedicated phone number (SMS), email, and news media. Swift then mashes those differing channels together into one feed, keeps track of where each item originated (it's source) and assigns a score to each source. This score is determined partly through user behavior and partly by our algorithms.
Meanwhile, our natural language computation service SiLCC, is used for what's called 'persistent tagging'. This is important because the act of tagging content is tedious and humans will often fail to do it. By using this natural language service, Swift can examine content and extract the keywords that are most relevant. For the headline "Major Earthquake in Chile", the important keywords are going to be 'earthquake' and 'chile'. These keywords can be used to find other content referencing the terms 'earthquake' or 'chile'. In essence that is 'predictive tagging', where algorithms try to extract meaning to help improve sorting. Users can vote on these tags to help our algorithms improve. Using these features, Swift users can determine the relevance of and relationship between content, regardless of the source.
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FRIENDS
Thomson Reuters/Open Calais, Meedan, GeoCommons, Appfrica, Ushahidi
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