Zaman says that rumor centrality is particularly valuable because it takes the entire network into account, not just the connections in the user’s immediate vicinity. For example, a person might have a lot of followers, but those followers might not be well-connected themselves. A person with fewer, better-connected followers has more paths for spreading information, and therefore a higher “rumor centrality” score.
Once they found a method of identifying superstars, the researchers built an experimental search engine around the system. Trumor finds people with high rumor centrality scores for a given topic and weights their posts, yielding pieces of information that are most likely to spread. Users can select a topic they want to search and be directed to pieces of information that could prove popular. The system does identify popular accounts, such as that of Lady Gaga, but, Zaman adds, it also pulls up relative unknowns. He says Trumor is still in its early stages, but adds that tests suggest it does well at identifying timely, pertinent information.
Other researchers are also looking at ways to measure influence on social networks. Abhik Das at the University of Texas at Austin has conducted studies on influence on cellular phone networks, and found that the structure of a network as a whole is a key factor. But his work also suggests that a person’s influence waxes and wanes over time, and that a good system must take this into account. “A person can’t go on spreading influence indefinitely,” Das says.
Zaman agrees, and says the plan is for future versions of Trumor to calculate rumor centrality for a window of time, such as the past week or month, allowing changes in the network to affect how information is weighted.