Does the structure of a social network tell us how much influence people have? In Twitter, this is definitely true. To spread information in Twitter, people do what is known as retweeting. These retweets form topic specific, growing networks. To model these networks, we developed a new network growth model called topological network growth which grows a network using rumor centrality as an influence function. It turns out that this model accurately captures several important properties of the retweet networks in Twitter, such as a power-law degree distribution and the existence of a single very high-degree node which we call a superstar.
Motivated by this empirical evidence, we developed a dynamic influence tracking engine for Twitter called Trumor which is based upon rumor centrality. Trumor has received media coverage, with stories about it appearing in the MIT Tech Review and CBS Smart Planet.
This work was done in collaboration with Devavrat Shah and
The figure on the right shows a screenshot of Trumor. One enters
a query, a start date, and a stop date, and clicks the Trumor Search button. Trumor then returns
all matching tweets in our database, with the tweets ranked by the Trumor score of their authors on the
retweet network for the topic.