 And now let's briefly look at a couple case studies. So last year we collected about 80 million tweets about the World Cup and we did some analysis on top using our tech and looking for patterns, trends, correlations and so on. And we observed some interesting stuff. So for instance, in this chart you see the volume of tweets in German over time and as you can see as Germany gained more and more momentum in the COP, German speaking people started engaging more and more with Twitter and we bought another and so on. And then we also measured the average sentiment towards teams and players and we looked for correlations between the rise and fall of sentiment and the events in these matches. And again found some interesting patterns. So for instance, in the Brazil versus Netherlands match, that's what we're seeing here, the sentiment average sentiment towards Netherlands started increasing and towards Brazil it started decreasing because they received three goals from Netherlands. And a more interesting example of sentiment can be seen in tweets mentioning Suarez. So this guy actually for those of you who don't know during one of the games he actually bit another player. And this is the average sentiment towards him, people's sentiment towards him throughout the World Cup. And as you can see he starts very positive and then when he bites the other guy it goes negative and then he issues an apology, it goes slightly positive and then it reaches a peak when he's signed by Barcelona so I guess take note of PR people, that's how you issue an apology.