Hi, my name is Chris Orua from I have research. I probably just take some of my time. So I just jump in straight to my slides. I leave my slides a key to the next thing. So we launched a research six months ago on how we crowdsource information from Twitter and we're more interested on violent events that were reported through Twitter and compared this on what they do on social media and also on the mainstream media. So we did actually go for information. We did ask for the information. We actually look for information that actually is there on the platform and what people volunteer to give in. So it's whatever we call passive crowdsourcing. So let's see if friends you had read clothes, nobody have a bunch of monitors. The use kind of to track everything is happening in our situation. We collected about 2.5 million tweets in about 30 days and actually using monitors and keywords, they were really worked for us. So we had to start thinking how to manage this big data. So we had the idea that let's use filters of keywords. Looking around on the tools that we had open source because you are pro open source, we couldn't find anything else. So we had the idea like how what we built something that actually is more of a smart filter to kind of go through this of this information. Then we hit an idea. It's not a problem having information overload is actually a filter failure. We don't have a good enough filter to give us what we need is straight on like any information like hey, there's something burning. Can you send red cross over there? Hey, there's some violence happening. Can you send Kenya police over there? So it's from part of our core research process and how to build something that actually give us tools because without that we can't actually compare what's happening when you have a whole corpus of information having online chat. You know, you have a problem with the customer's information is having about satire and jokes and normal chat. We didn't that. So as you can see at the end of it, we're able to show whatever Twitter had and whatever means the media has and kind of the cross section where what's happening with these two media. The rich way actually Twitter cannot be reached. So we tend to do machine learning, which is kind of the holy grail of analyzing data. So it's just a bunch of how to train your computer to understand that actually this is a whole these are people and this is kind of maybe a podium. So it's just a bunch of mathematical algorithms. You can tell if this is what kind of an incident looks like and this is not an incident and then it goes on to look for whatever is common between these instances and then from there you can actually build a predictive algorithm kind of give you more information. So with this, with all of how about we build a methodology whereby you sample some data using keywords, bringing it, have some people volunteers annotated as yes or no, whether it's new to us or not. And then pass it to a software whereby you can actually do the classification, build a model and then kind of do the prediction, but since it doesn't have enough data, you iterate through the whole process to actually get some bit of the data. Then as you can see, we kind of just do it comparison. If you do it manually, you have it to get around 10% of the data. If you use keywords, we're able to capture about let's say about say 10% of the news or the events, but when we used the algorithms, it kind of kicks to 85% about 68, then it jumped to 85. Then it enables now even break down the data into like hashtags, user names, names of places, how do you collect the data? In fact, around 80% of the instances we collected, they were from the location data. So from about 2.5 million tweets, we're able to move to around, give and take around 5,000, which are actually useful information. So it can show like now we have a tool that can easily go through straight and get data out of it. And then select what is the advantage of having this particular tool? So first of all, you can actually identify unique instances like Red Cross need to get very fast to a scene. Then again, you can do comparative analysis of was this incidents reported on the mainstream media was reported on any other crowdsourcing platform. And then also give this information to Red Cross or any other kind of organization. And then again, do also some other studies like, you know, that's teacher break news. So as you can see on the slides, we also have like we're able to have the locations. And then on the timeline when they were reported on this different media and actually if all this is kind of say important. So one of the things maybe like you take away from this particular talk is that we realize something very interesting about what we call the information dense environment. Even though mobile penetration is the same throughout Nairobi, but you have different location of it if the incident happens, it's never reported the same. Maybe the place like a ski base of the largest slums in Nairobi. You tend to have people do have mobile phones, but in case anything happens, you maybe have to write on a third party to give you the information. In a normal situation, you tend to have Facebook logins checking on four square and maybe Instagram photographs, but compared to the central Nairobi where people have normal chatter about whatever they're doing in the city. It's not far much different than when you get information, you're easy to get eyewitness accounts on this particular situation. So that's when Max is the end of my presentation. We are happy to generate more information to the humanitarian agency and your welcome to reader reports and join us in making a good tool.