  With the rapid growth of online social network sites (SNS), it has become
imperative for platform owners and online marketers to investigate what drives
content production on these platforms. However, previous research has found it
difficult to statistically model these factors from observational data due to
the inability to separately assess the effects of network formation and network
influence. In this paper, we adopt and enhance an actor-oriented
continuous-time model to jointly estimate the co-evolution of the users' social
network structure and their content production behavior using a Markov Chain
Monte Carlo (MCMC)- based simulation approach. Specifically, we offer a method
to analyze non-stationary and continuous behavior with network effects in the
presence of observable and unobservable covariates, similar to what is observed
in social media ecosystems. Leveraging a unique dataset from a large social
network site, we apply our model to data on university students across six
months to find that: 1) users tend to connect with others that have similar
posting behavior, 2) however, after doing so, users tend to diverge in posting
behavior, and 3) peer influences are sensitive to the strength of the posting
behavior. Further, our method provides researchers and practitioners with a
statistically rigorous approach to analyze network effects in observational
data. These results provide insights and recommendations for SNS platforms to
sustain an active and viable community.
