Technology

Study maps Twitter’s information ecosystem

New research outlines the six types of communities on the social network and what that means for communication

Twitter has provided a “special venue” for information sharing.
2013 Getty Images

If you are one of Twitter’s 241 million active users, there’s one constant to the network: it can be incredibly vast and remarkably small. The social network teems with an enormously diverse and intertwined group of people — from journalists and activists to government officials and college students — chatting on a range of topics in an often overwhelming ecosystem of cliques, crews and mobs. 

Finally, Twitter has something like a map.

A report from the Pew Research Internet Project highlights how researchers have developed six general models of the types of communities on the eight-year-old social network. As Pew's researchers see it, these visualizations represent the clearest picture of what civil society looks like when translated into the sprawling wasteland of the Internet.

“For a long time we’ve seen that social networks, the ones that are real and on display in places like Facebook and Twitter, are a special venue where people come together to learn and share things,” Lee Rainie, director of the Pew Research Internet Project, told Al Jazeera. “We want to use new tools to literally look at the shape of crowds. It feels like you’re taking an aerial photo of a crowd while having microphones buried in the ground to listen to what people are saying.”

The Pew Research Center normally produces public opinion polls describing American attitudes toward the Internet. To conduct this type of detailed social network analysis, Rainie and his team scoured the connections between Twitter users, the popularity of certain keywords and hashtags (like #My2K, for example, a White House campaign launched during November’s budget fight) and the density of mentions as people bantered back and forth on the service. The resulting analysis yielded six distinct social network structures that occurred regularly and could not be reduced to one another: a "polarized" crowd, a "tight" crowd, "brand clusters," "community clusters," a "broadcast network" and a "support network."

To anyone who relishes arguing about politics and policy on the Internet, a polarized crowd may look familiar. This structure represents discussions about a certain topic that feature two big and dense groups that have little connection to each other. It’s the equivalent of the liberal and conservative blogospheres that developed around the 2004 and 2008 presidential elections: while they discuss similar topics, the two groups are essentially ignoring each other and rely on very different information.
The six archetypal communities that develop on Twitter. / Pew Research Center

To anyone who relishes arguing about politics and policy on the Internet, a polarized crowd may look familiar. A polarized crowd represents discussions about a certain topic that feature two large and dense groups that have little connection to each other. It’s the equivalent of the liberal and conservative blogospheres that developed around the 2004 and 2008 presidential elections: while they discuss similar topics, the two groups essentially ignore each other and rely on very different information sources and arguments.

A “polarized crowd,” where two disparate groups discuss a topic independently of one another. / Pew Research Center

By contrast, “community clusters” represent popular topics that may develop multiple smaller groups, many of which form around a few particular hubs, each with its own audience, influencers and sources of information. Pew provides the example of a global news story that attracts different types of coverage from different news outlets, like the New York Times or the BBC, each with a unique following.

“Community clusters,” where several different groups with their own sets of influencers form around popular topics. / Pew Research Center

The topographical "maps" of these communities, generated by Pew using the data visualization tool NodeXL, aren’t just maps of relationships. They represent the channels of information in Twitter’s vast ecosystem, the roads and throughways, stoops and street corners in each topical neighborhood where users congregate and swap news and anecdotes.

“We’re not just looking at a social structure, but also the information that’s coursing around this circuit, this particular culture,” Rainie says. “You can see the information that has the most balance and who has the most influence. It complements the demographic snapshot we develop with public opinion data.”

But maps, of course, are tools for getting from one place to another, and the goal of this type of network analysis is to determine how the structure of communities shapes the spread of information across the social network’s millions of users. With 18 percent of American Internet users and 14 percent of the overall U.S. population on Twitter, understanding the geography of Twitter’s universe can help institutions and individuals understand how breaking news, gossip and misinformation propagate across the space.

For an institution with defined objectives like a political organization or a marketing firm, these maps of Twitter communities could be essential in helping to accomplish particular goals such as spreading a message, killing dissent or identifying potential allies and enemies in an online ecosystem where Americans are increasingly focusing their attention.

“If you’re a political actor, and if you manage to identify a certain social structure, it helps you figure out the way the world is working and what strategies are working,” Itai Himelboim, a professor at the University of Georgia who co-authored the report, told Al Jazeera. “But it also brings up questions about if you’re getting all the information you need to be a power broker. Are you missing out on alliances of constituencies? What’s the best place to spread your message, and who can you enlist to help you do this?”

Fil Menczer, a professor at the University of Indiana Bloomington School of Informatics and Computing, has researched the potential applications of this type of analysis for years. Menczer’s research touches on every aspect of Twitter’s role as a mirror for human communities, like examining the relationship between social data and the stock market, the spread of infectious diseases and how political campaigns manipulate data to spread misleading information. In a 2012 paper on the spread of memes on Twitter, Menczer and his team sought to demystify how information spreads on unrelated topics, yielding similar network structures to those uncovered by Pew.

(a) The #Japan meme shows how news about the March 2011 earthquake propagated. (b) The #GOP tag displays a strong polarization between people with opposing views. (c) and (d) Memes related to the Arab Spring and in particular the 2011 uprisings in #Egypt and #Syria display characteristic hub users and strong connections, respectively. / Courtesy of Indiana University

"Imagine, for example, that you’re interested in knowing what type of people are discussing a similar topic, and you find a certain structure with a clear hub; that is, people are following this source and retweeting and retweeting,” Menczer told Al Jazeera. “If I want to be influential with this topic, I have to get to this person. Or, on the other hand, if I am trying to promote a question that is against a particular meme, I can say ‘Well, if I discredit this person, that network will crumble.’”

But having a million followers doesn’t necessarily mean you’ll succeed. If a community of highly influential people is especially dense — meaning each member has several connections to other members — the influential hubs of that community can act as an "amplifier and a trap." An potential example could be several journalists with triple-digit Twitter followings tweeting to each other about a newsletter they all read.

“Things go into this network but don't come back out,” said Menczer. “Messages that tend to go truly viral tend to be those that are not trapped by a dense number of communities. It’s those that cross boundaries.”

One of the major lessons of network analysis, both Pew and Menczer emphasize, is that the Twitter commons hasn’t necessarily made society as democratic as techno-utopians would have you believe. Twitter isn’t a wide-open space, free of boundaries or obstacles: It’s a "mirror," as Menczer says, for the social structures of the real world.

“One of the presumptions about the rise of social media is that it’s changed everything,” says Himelboim. “In fact, if you look at the broadcast networks and brand clusters (two archetypes described by Pew), big, important and powerful institutions that wield tremendous influence offline still do on the Internet. This is really a reality check against those louder voices who claim the world has somehow been transformed."

“It makes you wonder about polarization in political discourse: Is this something that social media is responsible for?” asks Menczer. “Is more polarization easier because of social media, or are we observing what was already there with new technology? Or, even simpler: Would our discourse be better if Twitter and Facebook just didn’t exist?” 

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