A face in the crowd

There’s a dance going on in research these days. It’s the alluring dance of technology. Man versus machine. The problem is we’re letting the machine lead, which means we’re not using technology’s full potential.

The mistakes we make are most evident when we try to understand the networks that connect consumers. We see only the technology system that supports the network, not the human structure. As researchers, we’ve been trained to code and read the lowest common denominators of behaviour.

A person clicks the Like button on Facebook and the Like is recorded. That person becomes that Like – a fan of some brand or another, but the person is forgotten. As the Likes rack up, we lose further sight of the people behind the behaviour. By then, we’re looking at a movement. But by the time we see those numbers reach their high point, it’s too late to act upon them. Consumers are already moving on to the next thing. That doesn’t stop us piling in, trying to understand and explain what we’ve just witnessed. What we are conducting is essentially an autopsy. Research is stuck in a rut of quantifying and measuring after the fact.

Man versus machine

Researchers are in the habit of letting themselves be distracted by the technological platforms that enable human interaction. Yet the tools of our expression will come and go. First we jumped on MySpace. Then we moved to Facebook. We tweet now – but next year? Technologies are merely supportive systems for our expression. Technologies are not culture networks themselves.

Simply put, a culture network is like-minded individuals connecting, thriving and having impact – no matter how small. It’s these networks and their spontaneous expression that we need to be reading, as opposed to the artificial networks generated by brands. Likewise, reading the network needs to be less about quantifying the movement of the masses and its size, more about qualifying its impact.

We can’t ignore technology, of course. Technology, culture and networks are forever linked and move forward together. It wasn’t that long ago, after all, that the rave culture used digital networks to pioneer podcasting. And more recently the US-based Tea Party used digital networks to develop its position. Without technology, we might never have heard of them.

Getting ahead

Our best chance of seeing the future for a brand or a movement is by having a grasp of the codes that give it meaning and understanding the context of how it is evolving. To do that we have to know the narratives that are driving the story. The trick, then, for researchers is to get ahead of the big data challenge, and have the confidence to make sense of the natural flow of unstructured expression. There is rich information living in the language of these networks – written, visual and otherwise. We just need to get to grips with it.

Let’s take Occupy Wall Street (OWS) as a case in point. According to Douglas Schoen, writing for the Daily Beast blog, “OWS has already had a clear and demonstrable impact on both the Obama and Romney campaigns – arguably becoming the most important outside influence so far in this year’s election campaign dialogue.”

How did it get so big? The Occupy movement began as a local New York initiative that eventually gained global recognition. It is said to have begun on 17 September 2011. Had we been monitoring digital cues for dissatisfaction, we might have seen it coming long before.

Think about it: the internet meme #anonymous, a strong supporter of the OWS movement, is rich with visual material that appears on the ground at Occupy protests. #Anonymous had a life of its own before OWS, originating in 2003 on the imageboard 4chan. If you think of #anonymous as a digitised global brain, you can think of OWS as the localised expression of an accumulation of frustrations. For sure, these are two separate movements, but they do converge and diverge.

You can see their dance play out by looking at their shared imagery – through the ubiquitous use of Guy Fawkes masks (pictured) and the familiar protests against the Anti-Counterfeiting Trade Agreement and the Stop Online Piracy Act. Even the long-running internet meme Forever Alone – which began life on 4chan – frequently made an appearance at Occupy events.There is a tendency to wonder how many people are involved in the OWS movement, as if numbers matter. There is difficulty in counting the members of a demonstration that is so amorphous. In this case, numbers cannot quantify impact.

Similarly, we struggle to convey exactly what the movement is looking for. Noam Chomsky describes OWS as a movement that lacks concision. But ultimately that’s a good thing. Occupy has a lot to say that cannot be summed up in soundbites. That does not mean the movement cannot be read, empathised with or understood in a structured manner.

Sweeping the globe

Hundreds of cities around the world set up their own versions of Occupy. We could see the sweep of the cultural movement by tracking their tweets. It was fascinating to watch it spread through geo-location infographics. These showed clusters of localised people connected by threads across the globe.Monitoring Occupy by culture showed the movement was initiated by a coterie but gained steam through self-expression and pop culture – Angry Birds on t-shirts, McDonald’s Happy Meals slapped with Guy Fawkes stickers. The dialogue of those who started the movement in each city was very different from those who joined in along the way.We also found the Tea Party very much alive and active within the context of Occupy Wall Street.

And the more involved we found the Tea Party, the more engaged we found the Progressive (P2 ) movement. However, the two demonstrated very different patterns of migration. P2 was more evenly distributed across cultures, whereas the Tea Party was more densely populated in relation to collective society. In other words: when Occupy seized public icons and statues in famous squares, the Tea Party rallied. There are layers of context required to be read when exploring culture networks and there is a significant challenge in correctly interpreting the emerging data of these networks. But there is a risk too when we only measure the loudest signals within a cultural conversation. We have to figure our way out of this. We need to train our brains differently, to frame data, analyse signals and reveal patterns. Then we’ll truly have a platform that we can act on.