Well, first, thanks for having me this morning, excited to be here. we don't measure the sentiment. what we provide is the data that companies can use to measure sentiment or idea breaking news. we aggregate data from social networks like twitter or word press or stock twits and deliver that data to our clients that range from business intelligence platforms to pr agencies to hedge funds and they do analysis on it to make decisions. and so, give me an example of a situation where you got to sort of first move or look by really being able to look through the data online and then an investor able to take that information, make a profit. sure.
So the famous geopolitical example here is the osama bin laden death, which first broke on twitter about 20 minutes before it broke on more traditional news sites. you see this play out more distinctly across other news stories where the story may not break on one of these networks but what happens is the story quickly saturates faster than traditional news. for example if you go to our blog at gnip.com, yesterday's story the posting about the jpmorgan initial loss announcement a few months ago. we you saw that play out different ways across social media. stock is like a twitter for investors, 140 characters, you're talking about the market, because of that you saw an initial news spike and quickly saturated and everyone got a hold of that news, whereas something like twitter or word press there's a couple of differentitierations of the story breaking so it's more about capturing the catration of the story in the market, finding the breaking news component of that and looking for sentiment for specific things. how is it easy for a hedge fund that takes an algorithm and tries to sift through your data. sume there's got to be false positives in there.
Well, one there's a lot of data, that's the initial challenge is you have to be a fund that has the ability to do this and two, i know like any other data source you need to find what's trusted, not trusted and at an aggregate level what's valuable and what's not priced in. in the early adopters have been the funds that have this ability, the quantitative hedge funds, the ai funds, the guys who have 20 ph.d.s sitting in a room crunching data. we're starting to see the spread toward a more research case they can execute on. let's take a look at all the mentions of apple leading up to an earnings announcement and see if there's a way we can identify how well the iphone performed based on what people have said for the last three months. have there been mistaken situations where there's been a ®piece of data that has come through and people traded off the wrong way? there are stories broken on twitter that haven't been true. what we see is interesting the difference between what an individual sees on twitter and the fund the aggregate sees. to give you an example last year i guess there was a story about a bank in latvia and they didn't have the liquidity to mete obligations and two or three