In 2011, Kovas Boguta beautifully mapped the "Egypt Influence Network," showing Arab- and English-language tweeters -- and, perhaps most strikingly, the bridge nodes that spanned the linguistic divide.
For social scientists, big data sets and visualizations are already proving to be powerful tools. Writing about network diagrams of tweets around Iran's 2009 post-election crisis, researcher Devin Gaffney saw that by using tag clouds, "we can identify the key terms used in tweets," as he wrote. "By looking at different tag clouds over time, we can perhaps even see terms reflect a general shift from awareness/advocacy towards organization/mobilization, and eventually action/reaction."
Data scientists can also map the growth of ideas over time. In 2008, John Kelly, of Morningside Analytics, a company that specializes in social network analysis, visualized the Iranian blogosphere. When he did the same a year later and compared the maps, he discovered not only that the number of blogs had grown, but also that new segments had sprung up. One of them, which he dubbed “CyberShia,” revealed a dramatic increase in blogging activity by religiously oriented users. While this could point to an effort by the pro-regime Basij militia to contain dissident discourse on the Web, another theory sees the data as evidence of an intensifying debate about Islamic law and its role within the country’s political system.
Iranian blogosphere 2009:
Iranian blogosphere 2008:
Similarly, by looking back at social-media data emerging from the Arab Spring, it's possible to see political ideas congeal and take shape. By evaluating data from 2010 and 2011, the Arab Media Influence Report, which captured over 10 million online conversations per day, showed how discourse became politicized by the start of 2011. In the first quarter of 2010, 57 percent of the Arabic conversations on social media included socio-economic terms (such as income, housing, and minimum wage). By 2011, that number had dropped to 37 percent. In 2010, 35 percent of the conversations on social media included political terms such as revolution, corruption, and freedom). In 2011, the number shot up to 88 percent.
Of course, we hardly need social media data to tell us that Arab society was becoming more politically aware in the period leading up to the revolts. Arguably one might have arrived at a comparable conclusion by sitting in Cairo coffee houses for a year or by tracking debates in the Arabic press.
Data visualization really comes into its own when it allows us to see patterns we might otherwise have missed, patterns that can be modeled and applied to other contexts. If, as some sociologists believe, structure is destiny, then network graphs might be able to tell us about the life spans of political movements, their likely growth and their eventual demise. "People tend to think about the qualities of the individual when they figure out whether they will be likely to succeed or fail," says Marc A. Smith, the director of the Social Media Research Foundation, a California-based nonprofit. "Network theory people, and sociologists more generally, like to think about the properties of a person's network as having equal, if not greater consequence to their likely outcomes."