Skip to 0 minutes and 4 secondsHi, Suzy. Hi, Tobias. How did you enjoy this week? It was great. I really loved the topic of happiness, and we've covered a lot of important topics relating to ethics and privacy this week. Absolutely. Happiness is such a fantastic topic, and it's so important to study how we get happy. OK. So as usual, I've got some questions for you. Are you ready? Yeah. Let's go. Yes. OK. So to start with, what's the point of measuring happiness? That's a good question.
Skip to 0 minutes and 32 secondsI saw that a number of the learners brought this up and I think it comes back to the point that if you ask somebody whether it matters to them whether they're happy or not, most people would say that it does. But until now it's been quite difficult for us to understand in great detail what affects our happiness-- what makes us happy and what makes us feel not as good about the world-- simply because it's difficult to measure something so subjective as happiness. You have to ask people, generally, whether they're happy or not and this can be very time consuming.
Skip to 1 minute and 7 secondsSo this week we've seen some new ways of measuring happiness, either by asking people whether they're happy, or indeed, trying to measure particular aspects of their language which might give this away. And this gives us new ways to get large scale quantitative measurements of whether people are happy or not, and understand how these measurements might relate to measurements for other things we have about the world, so that we can understand what's influencing people's happiness-- what's making them happy or making them less happy. But I'm answering this question, Chanuki, knowing that, actually, you know an awful lot about this, about getting large scale, subjective measurements of our experience of the world.
Skip to 1 minute and 50 secondsDo you, perhaps, want to say a bit about your research? Because I know this is so cutting edge it's not even in the MOOC, but I think people might be quite interested to hear what you've been up to. Yeah. Something I've been working on-- basically some of you may be living in the countryside or some of you may go on holiday to these really beautiful settings, you'll probably notice that suddenly you just feel really good. So I was curious as to whether we could quantify that.
Skip to 2 minutes and 16 secondsSo there's a website called 'Scenic or Not', where basically you can rate all these pictures around the United Kingdom between 1 and 10, which basically gives you a map of scenicness all around the United Kingdom. So we compared this with how people felt about their health. I mean we will also look at how this makes people feel happy, as well. And when it comes to health, it turns out that people that live in these more scenic areas actually feel a lot more healthy. So I'm really interested in what we're going to find out-- if it actually also makes you feel happy. So keep tuned. I know we're all really excited to see this research published.
Skip to 2 minutes and 53 secondsI think you've done a brilliant job of this, Chanuki. So fingers crossed. We'll try and keep the MOOC learners up to date via Twitter on how this is progressing in the future. I've got another question for you, Chanuki, because I know you've thought about this a lot. How do you go about measuring happiness when happiness might mean something different to different people? Yeah, that's a really interesting question. So usually what was happening a while ago was a classic way of measuring happiness, by measuring the country's GDP. So that's basically how rich a country is. But of course, money doesn't necessarily equate to happiness. So there's been these really big debates about what does it mean to be happy.
Skip to 3 minutes and 32 secondsAnd it's usually measured by something called subjective well being. And, of course, there's all sorts of ways of measuring this. So sometimes you can measure it by life satisfaction, sometimes they use measures of mental distress, and also lists like we heard from Karen this week. With a mobile phone you can measure your happiness in several points. What this does is that say you're a generally a happy person so you're always saying, yes-- between a scale of one to 10 you're saying, 10, you're happy all the time. But every once in a while you may dip to seven.
Skip to 4 minutes and 6 secondsSo what we can do with these repeated point measurements is to actually see how a person individually when they feel happy and figure out why. So it doesn't matter that people might have different levels of happiness. We can still find out what's actually affecting their behaviour.
Skip to 4 minutes and 24 secondsOh, wow. I think it's a really important topic. And so hopefully this week we've managed to convey that there are these new ways of finally trying to measure happiness and that this gives us an opportunity to try and understand what things might affect how happy people are. So I think you had some more questions. Yes, I do. It's a question for you, Tobias. Can self-reported data be reliable? Are people likely to report what they want other people to hear? I mean how do you deal with this in your analysis? That's a very good question, Chanuki. And I wish we would always have the answer to that. So how to respond to your question.
Skip to 5 minutes and 5 secondsBasically, it depends on the kind of measurement which you have set up. And we have seen over the past few weeks a lot of different examples of how we can measure human activity. So sometimes we rely on, basically, measurements which humans leave behind by just going along their daily lives by buying bread in the supermarket or having a ride on the tube or just calling a friend for a chat. So this data is quite objective if we interpret it correctly.
Skip to 5 minutes and 41 secondsObviously, you are asking about something slightly different if we actually ask people to respond to questions like George did in Mappiness, in the app in which iPhone users were prompted to rate how happy they are in this moment in time. So the crucial aspect is to provide incentives for people, first of all, to do it-- to respond to these questions. And I think the key is this incentive because if the incentive is right, then it's not only an incentive to participate in this kind of questionnaire, but also to give a more or less correct answer. And in this concrete example, Mappiness, and what George has set up, people get something out if it.
Skip to 6 minutes and 27 secondsSo they actually get the a daily graph and some analytics when they are most happy and which places and so on and so forth. So for them it's actually an incentive or it would be good for them not to manipulate their happiness. And if you compare it to traditional ways of how happiness was measured, particularly in the social science research area, then you have to say that this was basically paper based, right? So people got a book and they had to write down when they were happy and what they were doing at that point in time.
Skip to 7 minutes and 0 secondsAnd obviously, this is something if you have forgotten you could just have done this two weeks later, just filling out all the empty rows and all the empty boxes in one go. And this is probably less accurate than actually a measurement which prompts you to do something. And what George also has emphasised is that there's only a certain time window within which responses are counted. So I think there are ways like this in order to make sure that you maximise the true response, so to say. There are also ways, in terms of providing incentives not to lie.
Skip to 7 minutes and 40 secondsAnd on the other hand, if you take all of this together, then you always need to evaluate on an aggregate basis whether the signal you get from the system is really in line with what you expect. Then also your hypotheses and what you know about social sciences theory becomes crucially import so that it's not disconnected from what you're measuring. So I think all these individual steps can help you to improve the quality of your self-reported data sets, but obviously you can't be sure that all of the responses are actually true. And also, this is something you need to account for in your analysis. Thanks, both. It's been a great week. So what's coming up next?
Skip to 8 minutes and 30 secondsSo next week-- we mentioned in the email last week that-- last week was quite exciting. Our brilliant PhD student, Federico Botta, had some of his research featured on the BBC, a paper that was published last week. And so in rather good timing, next week we're going to hear Federico talking about what he's found. So he's been looking at how you can measure the signs of a crowd using data from both mobile phones and also Twitter. And we'll also be looking at how you can use big data to work out not only where people are at the moment, but also where they're gong to go next. Right. We have a really exciting week coming up.
Skip to 9 minutes and 9 secondsMake sure you're not only watching Federico's wonderful talk which Suzy just mentioned, but also the interview he gave BBC World, which is truly fantastic and outstanding. And actually, it is part of the measurement of crowds, but a more general topic in terms of disasters and what we can do with big data. See you next week. Excellent. Looking forward to it. See you next week. Bye. Bye-bye. Bye.
Week 7 round-up
In Week 7, we began to explore how big data might help us measure and improve our happiness. Here’s a brief summary to help you prepare for Week 8.
You learned how George MacKerron created a smartphone app, Mappiness, to find out where and when people are happy all around the UK. You heard about a Facebook study which investigated whether emotions are contagious, by manipulating what Facebook users saw on their news feed. Thore Graepel also demonstrated that what we “like” on Facebook might give away all sorts of information about our personality, from how intelligent we are, to how satisfied we are with our lives.
These studies again raised important issues about privacy and the ethics of big data. It was great to read all your comments on where you thought the line should be drawn between what is acceptable for government and businesses to do and what you thought may be a step too far.
Finally, you started analysing Google Trends data in R and RStudio. You calculated the Future Orientation Index for the UK in 2012 yourselves. Well done!
This week, we move on to understanding how big data can help us measure where people are – something which is of extreme importance for the avoidance of crowd disasters. We mentioned in the video how excited we are about a new study we published with our excellent PhD student, Federico Botta. You can hear all about it from Federico in Step 8.3. In the meantime, here are links to a live interview Federico gave for BBC World News, as well as an article on the BBC News website:
Have a great week!
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