Group Member: J. L. Toole (ESD-PhD Student, MIT).
a. The cumulative total number of U.S. Twitter users for each week, normalized over the maximum, compared with normalized Google search and news volumes during the same period. b. Temporal snapshots of critical mass achievement at locations across the US. For each time, the smaller, gray markers indicate locations that have already reached critical mass at that time. The larger, black markers denote locations that achieved critical mass during that week. c. Simulation results are compared to actual critical mass achievement times for different subsets of locations (red symbols). Borrowing from the diffusion of innovations literature, we use four groups: Early adopting, Early Majority, Late Majority, Laggards, results shown for two groups.
While there has been much work examining the affects of social network structure on innovation adoption, models to date have lacked important features such as meta-populations reﬂecting real geography or inﬂuence from mass media forces. In a recent work, we show these are features crucial to producing more accurate predictions of a social contagion and technology adoption at the city level.
Impact: It is interesting to compare and contrast the spatial diffusion of web apps such as Twitter, with more tangible products such as gadgets, medicine, or cars. For example, it may be possible to use the composition of the cities as characterized by the adoption of Twitter to predict or even try to accelerate the adoption of other related kinds of technological innovations. We plan to make advances in models of spreading in networks where the roll of demographics, i.e. node attributes, as well as geography is critical for future predictions. These insights may be particularly useful in modeling opinion spreading such as in elections and collective action. (see paper)