The New Social Science

The Facebook IPO furthered the emergence of a new vernacular and brought into the forefront of US culture and society the systematic study of culture and society. The NY Times alone had story after story on what has come to be called ‘Big Data.’ Hereherehere, and here are just four examples.

At the center of Facebook’s meteoric rise — as a social media site and as an economic owner of data worth possibly $100 billion — is the question: why are these data valuable? Why does one care what his co-worker from two jobs ago ate for breakfast? And whether someone decided to ‘like’ this? Why is this interaction important? Why is it ‘data’? And therefore what’s the point of Big Data?

Big data are about the massive attempt through media technologies to gather in searchable and analyzable form databases of social-psychological dispositions, cultural patterns, knowledge, beliefs, and desires. It is about the gathering of opinions, statements, arguments, searches, and assertions. Big Data are about the privatization of a social-psychological sociological perspective and the turning of this perspective into a business model. Those who master the ability to gather significant social data — data that prove to be insightful, rather than data that don’t — will economically win out.

Question one: But aren’t opinions, assertions, whatever, the opposite of fact? A social-science business model in which the important data are opinions and not facts? How does that make $100 billion of sense?

Answer: Opinions are the meanings that animate facts and make them facts. A fact is best understood as an opinion/belief that is granted an audience of rightness. For example, there are data showing low tax rates lead to economic growth, and there are data showing the opposite. There are empirical refutations of both sides, too. The fact in this case is what you believe, what people believe, what patterns of people come to believe. In short: fact is assigned to the presentations of fact to which people attach an audience of implied rightness. Big Data is a business model contending to collect and store objective facts that are really the understanding of socialized understandings.

Question two: Do Big Data gather well enough the “understandings” — or meanings — that make their data valuable?

Ultimately, I don’t know the answer to this, but my hunch is that Big Data analytical styles do not yet understand the methods best built to study “meanings.” My hunch is Big Data will lead to consistent, dependable growth over time only once Big Data incorporate methods from qualitative sociology, which has put the empirical study of meanings at the center of its enterprise for decades now. By extension and also necessary, I think, will be the theoretical categories of social theorists Bourdieu, Habermas, Giddens, and Alexander — all system or structural theorists who, in one way or another, placed culture and knowledge (“meanings”) at the center of their respective, “multidimensional” analytical frameworks.

Big Data are about systems — systems of behavior — and, as Big Data recognizes as well as anyone, systems of behavior are built on social interactions and socialized beliefs. Big Data can and will adapt to what it does not yet know — how to best use qualitative data —and once it does, arguably the most advanced step ever recorded toward an objective, empirical, social science of human behavior will be realized.

(Twitter’s data, not Facebook’s, are most cutting edge, as I see it, so far.)

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