  We analyze five big data sets from a variety of online social networking
(OSN) systems and find that the growth dynamics of meme popularity exhibit
characteristically different behaviors. For example, there is linear growth
associated with online recommendation and sharing platforms, a plateaued (or an
``S''-shape) type of growth behavior in a web service devoted to helping users
to collect bookmarks, and an exponential increase on the largest and most
popular microblogging website in China. Does a universal mechanism with a
common set of dynamical rules exist, which can explain these empirically
observed, distinct growth behaviors? We provide an affirmative answer in this
paper. In particular, inspired by biomimicry to take advantage of cell
population growth dynamics in microbial ecology, we construct a base growth
model for meme popularity in OSNs. We then take into account human factors by
incorporating a general model of human interest dynamics into the base model.
The final hybrid model contains a small number of free parameters that can be
estimated purely from data. We demonstrate that our model is universal in the
sense that, with a few parameters estimated from data, it can successfully
predict the distinct meme growth dynamics. Our study represents a successful
effort to exploit principles in biology to understand online social behaviors
by incorporating the traditional microbial growth model into meme popularity.
Our model can be used to gain insights into critical issues such as
classification, robustness, optimization, and control of OSN systems.
