[MUSIC] So what are some of the examples of computational social science and there's now the following this last part will basically be a short overview of the content of the course. Because that's what we will do in this course in this introductory course, is to give you an overview of different examples of doing computational social science. And we will expose you to some of the methodologies involved, and we will start with our computation of scientific methods and you will this framework a lot. This framework will guide us through the entire course and we start here in the upper left corner with the empirical work. Which in computational social science is Big Data often as I already mentioned the digital footprint that we have, so the digital footprint is really a footprint. You can think about this as the digital footprint that we leave behind for example here with Twitter. And we just take this kind of data and then with that try to answer social questions. For example this here what you can see in this video, is when somebody in Twitter says, good morning or [FOREIGN] and we see wherever we have the digital footprint, we can also then see you. Actually we can see when people get up, you can find some interesting things from there. Nobody had to do a survey nobody had to do an experiment nobody had to ask anything to anybody to people just, get up and say good morning and now we can see some differences in when do get up. This year's using also Twitter data in Manhattan in New York and we can see where people from different ethnic backgrounds live. So these just see like in what languages do people write on Twitter and you can identify some Russian community some Koreans, some Japanese communities. Again, nobody did a survey nobody asked anything, but now very clearly actually by block we can identify where people from different ethnic origins live. Because that's the language they also tweet in, so they basically kind of like mapping out the world and you with this digital footprint. Now this time we're mapping out the world, we're not mapping it out geographically, but socially, so we're doing social side we advance with that a lot. So, in the past we made maps that kind of like look like this that's a Monrovia capitals that city in Africa and for a few hundred years we mapped out the geography of these cities. And we knew that in Africa that's what it actually looks like now, at the same time, especially in Africa only half of people have a birth certificate. So actually we don't know how many people there are actually really we don't have any register, we don't have any solid empirical evidence to do social science. The government doesn't know how many people actually live in their country. So mapping out this this scenario social is kind of like the first map makers when they would write about there be dragons. All right, we don't know what's behind that, but so this map was that in August 2014 only a few months later in November 2014, that's what the map looks like. All right, so again we go from there be dragons here to that and they could fill out this map knowing actually where people are, how did they do that, what do you think? They use cell phone they use the digital footprint that people leave behind with their cell phone and almost everybody even Africa nowadays has the cell phone. So we don't know how many people there are, but if a cell phone walks around the street, we assume that a person is attached to it. [LAUGH] All right, that's the best evidence we have that that there are people and how many they are. So in this sense, we're measuring out the world and you again with this digital footprint we can ask a lot of questions. Social science questions that before were either too costly to ask them or also to study, I already talked about the problems of relationships. For example, we could look at the digital footprint of what people are worried about in their relationships, basically what their search on the Internet. So if we look at that what words go together with the words like marriage or relationship, we see the word sexless marriage is by far the biggest concern. Whereas relationships that's not the biggest problem, relationship sexless is the second concern that people have. And make other comparison for example, if we see what husbands and wives search on the Internet, we can see that the search for my husband won't have sex with me and my wife won't have sex with me. That's kind of like equal, right here we have both about 1000, so that's kind of like an equal concern among husbands and wives. However now if you go into relationships, we can see that my boyfriend won't have sex with me. Is twice as big of a concern then my girlfriend won't have sex with me. [LAUGH] So yeah, I don't know what's up with all these boyfriends right. [LAUGH] Now, so this is again a digital footprint, it might be that they're sampling issues. Maybe girlfriends search that more on the Internet and boyfriends not so there's concerns we will have to talk about when we talk with that. And if you do computational social science, but there is a lot of more evidence out there. The founder of this dating website called OkCupid also wrote an interesting book and he basically studied what people are doing on this dating website. And one thing he studied is, what are the age of for example of women, and what age of profiles of man do they look at right? So here he mapped it out, so we have here the age of women and we can see that women in their 20s. So for example with 24, they look for for guys who are a little bit older, right? So 24 years old woman would look for a guy who is like 25, 26, whereas the ladies in their 40s, for example with with 46, 47. They would look for younger guys, you know in the late 30s, and so that's why the diagonal is kind of like crossed rider. The younger ones look for the older ones and the older ones look for their little bit younger ones. So very interesting just it's the digital footprint, they didn't produce it really they just look for that and now we can have evidence of it by studying this digital. What do you think, how does this graph look for men? Yeah, right, really that's what he found, that's what it looks like independent from the age of man. They always look at profiles of women who are in their early 20s, like always, and you know traditionally that's kind of like that's kind of like been a joke. It's been a kind of like being a running gag, so for example, you would say it is like a party jog, right? Is hey, dude, women do the always say we men are not consistent, we are very consistent. We always like women in their twenties, which is, it's a joke and we laugh about it. But now we have empirical evidence about it, right and having this empirical evidence actually makes you think that being a social science researcher. Living in a society with a divorce rate of 50% having a big proportion of children growing up with only one of their biological parents, right. Building societies like that you start to think like, what kind of species are we? All right, so here these people did not know they were observed, it's a very big footprint of millions of people, and that's just naturally what they do. So, now we have the tools of the to ask these questions to go deeper into trying to understand what's going on with this kind of weird thing that we call humans. And what kind of species we are and try to figure that out in order to see how we can make also society better. And given all these opportunities, often people say that what this Big Data digital footprint is it's kind of like equivalent to what the telescope was for astronomy, right? So before the Incas and in ancient cultures, they already looked at the stars and they mapped out an amazing amount of stuff. But with the telescope, we convert it astronomy into a science. We could finally see and we could see very far with these kind of telescope, and they say the digital footprint allows us to finally see society with this level of granularity, or what the microscope was for biology. We always had an idea the kind of like that for sales actually work and behave but the microscope allowed us to see them. And with that biology became a really strong science that allowed us to make predictions because we could understand. And the digital footprint it's kind of like the equivalent of what the telescope was for astronomy and the microscope for biology. And during this course we will work with that, so we will work web scraping tools. So basically you have this digital footprint here on your favorite Internet side, and we develop some tools web scraping tools that then a machinery that helps us to derive data from that. And with this data, then we can make analysis so, we will develop some web scrapers together. You will develop a web scraper yourself we scrape some data in order then then to have some empirical observations about society and then to do some analysis. Which brings us to our second point of our computational scientific methods.
