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A couple of years ago I started using Twitter,
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and one of the things that really charmed me about Twitter
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is that people would wake up in the morning
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and they would say, "Good morning!"
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which I thought,
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I'm a Canadian,
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so I was a little bit,
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I liked that politeness.
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And so, I'm also a giant nerd,
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and so I wrote a computer program
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that would record 24 hours of everybody on Twitter
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saying, "Good morning!"
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And then I asked myself my favorite question,
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"What would that look like?"
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Well, as it turns out, I think it would look something like this.
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Right, so we'd see this wave of people
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saying, "Good morning!" across the world as they wake up.
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Now the green people, these are people that wake up
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at around 8 o'clock in the morning,
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Who wakes up at 8 o'clock or says, "Good morning!" at 8?
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And the orange people,
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they say, "Good morning!" around 9.
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And the red people, they say, "Good morning!" around 10.
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Yeah, more at 10's than, more at 10's than 8's.
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And actually if you look at this map,
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we can learn a little bit about how people wake up
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in different parts of the world.
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People on the West Coast, for example,
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they wake up a little bit later
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than those people on the East Coast.
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But that's not all that people say on Twitter, right?
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We also get these really important tweets, like,
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"I just landed in Orlando!! [plane sign, plane sign]"
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Or, or, "I just landed in Texas [exclamation point]!"
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Or "I just landed in Honduras!"
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These lists, they go on and on and on,
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all these people, right?
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So, on the outside, these people are just telling us
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something about how they're traveling.
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But we know the truth, don't we?
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These people are show-offs!
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They are showing off that they're in Cape Town and I'm not.
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So I thought, how can we take this vanity
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and turn it into utility?
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So using a similar approach that I did with "Good morning,"
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I mapped all those people's trips
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because I know where they're landing,
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they just told me,
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and I know where they live
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because they share that information on their Twitter profile.
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So what I'm able to do with 36 hours of Twitter
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is create a model of how people are traveling
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around the world during that 36 hours.
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And this is kind of a prototype
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because I think if we listen to everybody
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on Twitter and Facebook and the rest of our social media,
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we'd actually get a pretty clear picture
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of how people are traveling from one place to the other,
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which is actually turns out to be a very useful thing for scientists,
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particularly those who are studying how disease is spread.
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So, I work upstairs in the New York Times,
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and for the last two years,
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we've been working on a project called, "Cascade,"
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which in some ways is kind of similar to this one.
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But instead of modeling how people move,
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we're modeling how people talk.
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We're looking at what does a discussion look like.
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Well, here's an example.
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This is a discussion around an article called,
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"The Island Where People Forget to Die".
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It's about an island in Greece where people live
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a really, really, really, really, really, really long time.
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And what we're seeing here
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is we're seeing a conversation that's stemming
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from that first tweet down in the bottom, left-hand corner.
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So we get to see the scope of this conversation
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over about 9 hours right now,
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we're going to creep up to 12 hours here in a second.
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But, we can also see what that conversation
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looks like in three dimensions.
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And that three-dimensional view is actually much more useful for us.
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As humans, we are really used to things
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that are structured as three dimensions.
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So, we can look at those little off-shoots of conversation,
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we can find out what exactly happened.
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And this is an interactive, exploratory tool
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so we can go through every step in the conversation.
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We can look at who the people were,
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what they said,
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how old they are,
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where they live,
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who follows them,
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and so on, and so on, and so on.
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So, the Times creates about 6,500 pieces of content every month,
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and we can model every single one
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of the conversations that happen around them.
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And they look somewhat different.
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Depending on the story
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and depending on how fast people are talking about it
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and how far the conversation spreads,
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these structures, which I call these conversational architectures,
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end up looking different.
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So, these projects that I've shown you,
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I think they all involve the same thing:
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we can take small pieces of data
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and by putting them together,
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we can generate more value,
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we can do more exciting things with them.
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But so far we've only talked about Twitter, right?
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And Twitter isn't all the data.
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We learned a moment ago
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that there is tons and tons,
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tons more data out there.
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And specifically, I want you to think about one type of data
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because all of you guys,
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everybody in this audience, we,
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we, me as well,
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are data-making machines.
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We are producing data all the time.
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Every single one of us, we're producing data.
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Somebody else, though, is storing that data.
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Usually we put our trust into companies to store that data,
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but what I want to suggest here
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is that rather than putting our trust
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in companies to store that data,
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we should put the trust in ourselves
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because we actually own that data.
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Right, that is something we should remember.
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Everything that someone else measures about you,
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you actually own.
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So, it's my hope,
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maybe because I'm a Canadian,
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that all of us can come together
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with this really valuable data that we've been storing,
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and we can collectively launch that data
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toward some of the world's most difficulty problems
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because big data can solve big problems,
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but I think it can do it the best
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if it's all of us who are in control.
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Thank you.
