As an example Smith cites two visualizations he made of the Occupy movement and the Tea Party on Twitter. In his renderings, the Tea Party appears as a far more tight-knit group, with many of them following each other, whereas Occupy is made up of looser clusters with a few high-profile accounts receiving plenty of retweets. In the lower right-hand corner of the visualizations there is a grid, a matrix of "isolates:" People who are talking about the ideas of Occupy or the Tea Party, but who don't have connections to others on the graph. For Occupy, the number of isolates is greater, which according to Smith could indicate a larger potential for growth and stronger brand cachet.
TeaParty on Twitter:
Occupy on Twitter:
When interrogating the data, the answers they yield are only as good as the questions we ask. Looking at the Twitter data obviously doesn't tell us how the Occupy forces in Zuccotti Park behave, sound, or smell. (The Twitterverse, after all, is not the universe.) But as Smith notes, fleshing out the data shows that that the group's structure strikingly reflects its self-description as a decentralized and bottom-up movement. "There's no question that big data can be very, very useful," says Zeynep Tufekci, a sociologist at the University of North Carolina. "But it's less useful and even misleading at times if it is not evaluated by people who understand the context of what they're looking for."
Without any context about Internet penetration or the demographic of Twitter users, network diagrams of Egyptian tweeters can give us the impression that Twitter played an oversized role in the unrest there. We might also get an overblown impression of the liberal character of the Arab Spring. But instead of being broadly representative of Egyptian society, these tweeters are rather a small sub-set of young, educated, often English-speaking elites, with a propensity for liberal ideas. They are perhaps as revealing about Internet connectivity as they are about Egyptian society. (Gregor Aische, a German designer, has produced his own striking visualization of the global digital divide.)
But perhaps it helps to ask a different question. If we want to analyze what's going on in the minds of Egypt's most influential elites, then that social media data set can certainly offer useful answers. "That may only be a sampling of a small [segment] of population, but if that's how they're organizing and communicating, then that's an important population," says Noah Iliinsky, an expert in the theory and practice of information visualization. "So I wouldn't discount that population simply because it's not representative."
What has got businesses, governments, and academics excited about big data and visualization is the ability to detect patterns in real time rather than mapping perceptions post-factum -- and even to use this data to make predictions. We're already seeing cases in which visualized data enable policy makers to make quick informed choices about public health, poverty, or energy efficiency. Google Flu Trends, which estimates current flu activity around the world in real-time by monitoring search terms, has been shown to predict confirmed cases of flu with a level of accuracy comparable to the Centers for Disease Control and Prevention. In 2010, researchers managed to predict with an accuracy of 87.6 percent the daily changes in the closing values of the Dow Jones Industrial Average by analyzing Twitter users' moods.
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