  Research on the growth of online tagging systems not only is interesting in
its own right, but also yields insights for website management and semantic web
analysis. Traditional models that describing the growth of online systems can
be divided between linear and nonlinear versions. Linear models, including the
BA model (Brabasi and Albert, 1999), assume that the average activity of users
is a constant independent of population. Hence the total activity is a linear
function of population. On the contrary, nonlinear models suggest that the
average activity is affected by the size of the population and the total
activity is a nonlinear function of population. In the current study,
supporting evidences for the nonlinear growth assumption are obtained from data
on Internet users' tagging behavior. A power law relationship between the
number of new tags (F) and the population (P), which can be expressed as F ~ P
^ gamma (gamma > 1), is found. I call this pattern accelerating growth and find
it relates the to time-invariant heterogeneity in individual activities. I also
show how a greater heterogeneity leads to a faster growth.
