A Coevolution Model of Network Structure and User Behavior: The Case of Content Generation in Online Social Networks
| العنوان: | A Coevolution Model of Network Structure and User Behavior: The Case of Content Generation in Online Social Networks |
|---|---|
| المؤلفون: | Tuan Q. Phan, Prasanta Bhattacharya, Xue Bai, Edoardo M. Airoldi |
| المصدر: | Information Systems Research. 30:117-132 |
| بيانات النشر: | Institute for Operations Research and the Management Sciences (INFORMS), 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Information Systems and Management, Social network, Computer Networks and Communications, business.industry, Computer science, 05 social sciences, Network structure, 02 engineering and technology, Library and Information Sciences, Management Information Systems, World Wide Web, 020204 information systems, 0502 economics and business, Content generation, 0202 electrical engineering, electronic engineering, information engineering, Peer influence, 050211 marketing, Content production, business, Coevolution, Information Systems |
| الوصف: | With the rapid growth of online social network sites (SNSs), it has become imperative for platform owners and online marketers to quantify what factors drive content production on these platforms. Previous research identified challenges in modeling these factors statistically using observational data, where the key difficulty is the inability of conventional methods to disentangle the effects of network formation and network influence on content generation from the subsequent feedback effect of newly generated content on network structure. In this paper, we adopt and enhance an actor-oriented continuous-time statistical model that enables the joint estimation of the coevolution of the users’ social network structure and of the amount of content they produce, using a Markov chain Monte Carlo–based simulation approach. Specifically, we offer a method to analyze nonstationary and continuous-time behavioral data, typically recorded in social media ecosystems, in the presence of network effects and other observable and unobservable user-specific covariates. The proposed method can help disentangle network effects of interest from feedback effects on the network. We apply our model to social network and public posting data over six months to find that (1) users tend to connect with others that have similar posting behavior; (2) however, after doing so, these users tend to diverge in their posting behavior, and (3) peer influence effects are sensitive to the strength of the posting behavior. More broadly, the proposed method provides researchers and practitioners with a statistically rigorous approach to analyze network effects in observational data. Our results lead to insights and recommendations for SNS platform owners on how to sustain an active and viable community. The online appendix is available at https://doi.org/10.1287/isre.2018.0790Test . |
| تدمد: | 1526-5536 1047-7047 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_________::b6250302b270b8b8d76d0b856b164f32Test https://doi.org/10.1287/isre.2018.0790Test |
| رقم الانضمام: | edsair.doi...........b6250302b270b8b8d76d0b856b164f32 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15265536 10477047 |
|---|