This article was originally published on PRNEWS here.
When it comes to the social sciences, figuring out how people interact with society to arrive at decisions (such as purchasing choices), ranks up there with splitting the atom or breaking the 4-minute mile. Now, thanks to advances in the computer sciences being applied to social sciences, this looks increasingly possible. Welcome to the world of computational social science, in which scientists use computers to study social phenomena. Think of it as a marriage between computer and social sciences.
There’s nothing new under the sun, and, in the clear light of day, computational social science, too, is revealed to be made of old parts, just repurposed to reach an astounding scale. It’s not like social scientists haven’t been using computers to study social phenomena for a long time, but now new advances in computer science make more things possible. It’s not too terribly different than going from multiplexes to being able to stream nearly every single movie ever made on your telephone. It’s still movies, but so much more is available to watch.
But, because of the limitations of access to large sets of data, social scientists needed to do field research or entice college students with free pizza to participate in studies. The issues facing social science were too complex, the observational data too hard to obtain, and the large-scale social networks too difficult to manipulate for social science. This limited social sciences to the realms of solo authors of papers and books and lead to the perception that social sciences were “soft sciences.”
No more. Now, those same college students—as well as most everyone else in the word—are throwing off digital flotsam with every post, purchase, or picture. Mankind’s digital exhaust is social science’s Big Data, and it’s brought this so-called soft science out of the library and into the lab alongside biology which made a similar shift in the 1990s, representing a larger conversion from analogue to digital analytics. In the span of a decade, social scientists have previously unimaginable reems of data and new computing power capable of harnessing new thinking and new possibilities.
Now social scientists can track them through all the data that we all leave behind and learn that many of what we thought were our own choices were actually the results of social contagions. Colds go viral, as do memes. Doesn’t it make sense that our ideas and preferences do, too?
Because everything digital is measurable, and most human behavior now leaves a digital footprint, we have at least the theoretical capacity to measure humanity’s complexities. Now we don’t need to just rely on snapshots of what people say they think, we can actually achieve a deeper understanding of how they act and interact in their networks.
It’s not just about the conscious acts on social media or purchasing decisions that can be measured. In his book, Honest Signals: How They Shape Our World, MIT’s Alex Pentland describes figuring out how to turn measuring our nonverbal communication into accurate predictions of how certain social interactions, such as job interviews or dates, would work out. He did this a decade ago with sensors hanging from lanyards. Imagine the data that could be collected, with permission of course, by our phones or digital assistants, and what computational social science could discover about us.
Computational social sciences are not a futuristic fantasy. Already, government agencies such as the National Security Agency and DARPA, and large, data-driven corporations such as Google and Yahoo, are hiring social scientists to engage in computational social science. And, though top universities such as the University of Chicago, Stanford, and Cornell are creating new departments and offering graduate degrees in this burgeoning field, there are at present many more opportunities in computational social science than there are computational social scientists.
To get an idea of how many jobs will need to be filled in computational social science, think of all the fields this touches: computational economics, computational sociology, cliodymanics, culturomics, and the automated analysis of contents, such as the investigation of behavioral and societal relationships. No one data scientist and no one social scientist can handle this. Demand for people with the right expertise is going to expand exponentially as new applications for computational social science are discovered.
And the applications for computational social science are profound. Brands could decode makes ideas spread on their own, who and what influences influencers. Inevitably, this will spread to the rest of the communications industry, and it’s time for us to prepare. As an industry we must adopt the term computational communications and create standards for measurement, data, and ethics.
New data and new capabilities have brought us to this point. New institutions designed to foster not just government collaboration with academia but also private applications will take us onward from this point. The possibilities, it seems, are endless.