Something quite revolutionary happened the other day, when Twitter official announced with this article, that it had introduced a "human computation engine" to more effectively identify topics and context belonging to their trending news themes and to what people search on Twitter.
To achieve this, Twitter has hired a small custom team of crowdsourced workers from Amazon / Mechanical Turk, and it feeds them in real-time with trending issues and topic they have to analyze on the basis of a specific set of questions. Here is a sample:
"What category does the query belong to? For example, [Stanford] may typically be an education-related query, but perhaps there's a football game between Stanford and Berkeley at the moment, in which case the current search intent would be sports.
- Does the query refer to a person? If so, who? And, what is their Twitter handle if they have one? For example, the query [Happy Birthday Harry] may be trending, but it's hard to know beforehand which of the numerous celebrities named Harry it's referring to.
Is it One Direction's Harry Styles, in which case the searcher is likely to be interested in teen pop? Harry Potter, in which case the searcher is likely to be interested in fantasy novels? Or someone else entirely?"
The article provides wonderful insight into how such a challenging task is carried out, and what measures have been taken to ensure highest quality results.
Check also what ReadWrite writes about it: http://readwrite.com/2013/01/08/watch-out-cnn-new-twitter-search-capabilities-will-rule-breaking-news
Pretty revolutionary. Insightful. Informative. 9/10