Tuesday, July 27. 2010
Interesting article in New Scientist about digital anthropology providing the sort of data that will allow it to become a science:
Social scientists have long had to rely on crude questionnaires or interviews to gather data to test their theories; methods marred by reporting bias and small survey sizes. For decades, the field has been looked down upon by some as a poor cousin to the hard sciences. The digital age is changing all that - practically overnight, the study of human behaviour and social interactions has switched, from having virtually no hard data to drowning in the stuff. As a result, an entirely different approach to social science has emerged, and studies based on it are appearing with increasing frequency. The impact has been remarkable.
"The data revolution is here for social science," says Albert-László Barabási of Northeastern University in Boston. "For the first time, scientists have a chance to study what humans do in real time and in an objective way. It's going to fundamentally change all fields of science that deal with humans."
Barabasi, old hands may recall, was one of the early thinkers on Social Networking as a predictive science. what Malcolm Gladwell called the Tipping Point. Curiously, one of the experimeters looking at how this worked was a Gladwell critic, Duncan Watts:
To examine what made some songs more successful than others, Watts and Salganik created a project called Music Lab. It featured a website where more than 14,000 people listened to any of 48 songs by relatively unknown bands, rated them, and downloaded them if they wanted. These options provided a measure of quality (the average rating given) and popularity (the number or downloads). Crucially, the duo were also able to control whether listeners could see how many times other people had downloaded any particular song, or instead had to rely solely on their own judgement. In this way, they could effectively compare outcomes with the power of social influence turned on or off. They also grouped the socially influenced participants into eight independent "worlds" so that they could explore how the outcomes - the popularity rankings of the various tunes, based on downloads - might change if the tape of history could be rewound and run again.
The results strongly support the idea that human influence has a huge effect in making some songs more popular than others. This factor also makes it much harder to predict what will happen, and which songs will do well. The worlds in which social influence was operating had much higher inequality - with popular songs going up and unpopular songs going down to an even greater extent than in the worlds lacking social influence. With social influence turned on, song popularities fluctuated wildly between one world to the next. So, like it or not, it seems like many of us follow the herd.
As we pointed out at the time, Watt's attack on Gladwell was more about positioning that point of order. The article goes on to talk about an experiment that shows we have two natures - an investgative and a sheep like following nature, and the one tips over into the other in much the same way as other systems change state -
Onnela and Reed-Tsochas realised that analogous changes take place in Facebook, on which people share their profiles with their online friends. Facebook users can also choose to install applications - software components that personalise their Facebook page. If one person adopts an app, their friends are automatically notified, and they can also see the apps their friends are using. Facebook users also have access to a list of popular applications, akin to a best-sellers list.
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The data stored on Facebook makes it possible to analyse the growth in popularity of individual applications in unprecedented detail. Onnela and Reed-Tsochas analysed the popularity of several thousand applications in 2007, shortly after those apps were introduced, and then studied how other users adopted them over time. They looked to see if the sequence of adoptions for each app followed an essentially random pattern - indicating that each "adoption event" was independent of other previous adoptions - or whether previous adoptions by a participant's friends influenced the likelihood of their subsequent adoption of an app.
The results showed that both independent thinking and copying behaviour play a role, reinforcing conclusions reached by conventional survey methods. However, the study also indicated there are two very different processes in action. On the one hand, their analysis shows, at first, when a new app appears it is adopted by users independently of their friends' opinion. But if the popularity of an app crosses a threshold, its very popularity then seems to draw many people to adopt it, and its growth can become explosive. Just as Watts and Salganik found in their Music Lab experiment.
"We found very distinct regimes in which individual or collective behaviour dominates. The change from one to the other is a sharp on/off process," says Reed-Tsochas. They don't yet know whether tipping points of this kind might influence real-world processes beyond the web, such as shifts in political opinions or the popularity of books. "It's certainly possible," says Reed-Tsochas, "but we'll need to wait for equally good data in those areas to find out."
Others are looking at Twitter's ability to predict movie popularity
Huberman and his colleague Sitarum Asur wondered if it might be possible to do even better by exploiting the enormous volume of opinion expressed through social media such as Facebook and Twitter. Opinions voiced in these media, they reasoned, should have strong predictive power because they actually play a role in determining which films do well. "What gets discussed through these media often ends up setting trends," says Huberman.
In an attempt to mine these opinions, they studied the chatter on the microblogging site Twitter. They started from the supposition that movies that get talked about a lot - that generate a lot of buzz - should end up being more popular. To measure the buzz for each film, they looked at the rate at which it generated tweets immediately following its release. They used this as a predictor of the ultimate film sales.
The results show that the rate at which movie tweets were generated can provide accurate predictions of box-office revenue, more accurate even than the Hollywood Stock Exchange. In the end, predicting successful movies may only be of interest to film companies and investors. But Asur and Huberman reckon this is just the beginning, and that their technique should be able to predict social outcomes of many kinds. "When properly tapped, social media express a collective wisdom which can yield an extremely powerful and accurate indicator of future outcomes," says Asur.
Huberman says such analyses could soon help predict many other events, such as election outcomes, or quickly gauge public reactions to major events, just as long as we have evidence reflecting peoples' views on the relevant issue. "Twitter and texting in general were influential in the election of Barack Obama and some businesses are already analysing these kinds of data to assess the likely success of their products," says Huberman.
(Cass Business School's Dr Caroline Wiertz presented corollary findings at TEDxTUttle II, by the way). Anyway, what is emerging is something akin to Isaac Asimov's science of Psychohistory - we are all different, but en masse we are remarkably predictable:
It's the discovery of underlying patterns of this kind that has excited so many scientists. Given the undeniable complexity of individual human beings, it's not as if social science is going to become like physics, grounded in eternal and general laws, but access to data on human events makes it possible to identify the patterns that do exist and these can be useful for demystifying the social world.
Interesting times...........................
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