When significant events happen people instantly flock to Twitter, and in particular searched on Twitter to discover what was happening. Without a very specific context it is often impossible to know what news clips mean.
Twitter built a real-time human computation engine to help it identify search queries as soon as they’re trending, send these queries to real humans to be judged, and then incorporate the human annotations into our back-end models. It utilizes thousands of micro-workers for a novel news service. Initially, this crowdsourcing will give them text analytics on demand to help them categorize news items for better search and better targeted contextual ads. But the same mechanisms could also be used for such editorial tasks as ranking for readability, impact, novelty, and substance. Still in experimental stages, it may end up being a disruptor.
Outsell’s analyst Alexander Linden writes in his recent Outsell Insight: “The information industry is a prime candidate both for benefiting from, and being challenged by, these new models of work. With improving bandwidth and high-resolution screens on mobile devices, the requirement of having a central location with all of its associated costs is rapidly diminishing.”