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• W2396189337 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 explicitly specify and discern the effects of network formation from 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, 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 and uncover novel insights: 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) friend selections are sensitive to the strength of the posting behavior. Our method provides researchers and practitioners a statistically rigorous approach to analyze network effects in observational data. By suitably applying this method, researchers can generate insights and recommendations for SNS platforms to sustain an active and viable online community." @default.
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• W2396189337 date "2015-01-01" @default.
• W2396189337 modified "2023-10-16" @default.
• W2396189337 title "Investigating the Impact of Network Effects on Content Generation: Evidence from a Large Online Student Network" @default.
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• W2396189337 doi "https://doi.org/10.2139/ssrn.2703994" @default.
• W2396189337 hasPublicationYear "2015" @default.
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