A Co-evolution Model of Network Structure and User Behavior in Online Social Networks: The Case of Network-Driven Content Generation

TL;DR

This paper introduces a novel actor-oriented continuous-time model to jointly analyze how social network structures and user content generation behaviors co-evolve on social media platforms, providing insights into peer influence and content dynamics.

Contribution

It develops an enhanced statistical model using MCMC to jointly estimate network formation and influence effects in social media data, addressing previous modeling limitations.

Findings

Users connect with others having similar posting behaviors.

Users tend to diverge in posting behavior after connecting.

Peer influence strength affects posting behavior dynamics.

Abstract

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.