Investigating The Impact Of Network Effects On Content Generation: Evidence From A Large Online Student Network

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 using observational data due to the inability to separate the effects of network formation from those of network influence. The inability to successfully separate these two mechanisms makes it difficult to interpret whether the observed behavior is a result of peer influence or merely indicative of a selection bias due to homophily. In this paper, we propose 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, similar to what is observed in social media ecosystems. Leveraging a unique dataset contributed by Facebook, we apply our model to data on university students across six months to find that users tend to connect with others that have similar posting behavior. However, after doing so, users tend to diverge in posting behavior. Further, we also discover that homophilous friend selection as well as susceptibility to peer influence are sensitive to the strength of the posting behaviour. Our results provide insights and recommendations for SNS platforms to sustain an active and viable community.