Network Analysis of Digital and Social Media
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ItemA Network View of Social Media Platform History: Social Structure, Dynamics and Content on YouTube( 2019-01-08)Social media sites are prone to change from many internal and external causes, yet it is difficult to directly explore their histories in terms of the content itself. Search and browsing features are biased toward new and paid content, archives are difficult to navigate systematically, and their scale makes any observations challenging to contextualize. Here, we present results of an ongoing study of YouTube’s history (currently with more than 76 million videos) using a combination of iterative browsing, network crawling and clustering within and across time periods. Through this method, we are able to identify historical patterns in YouTube's content related to internal and external events. Our approach thus illustrates an adaptation of network analysis for understanding genre evolution in the histories of social media platforms.
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ItemThe Network Structure of Successful Collaboration in Wikipedia( 2019-01-08)Wikipedia is one of the largest and most successful examples of decentralized peer-production systems currently in existence. Yet, the quality of Wikipedia articles varies widely with articles considered of encyclopedic quality (called featured articles) representing less than 0.1 percent of all articles. In this paper, we examine how article quality varies as a function of the network mechanisms that control the interaction among contributors. More specifically, we compare the network mechanisms underlying the production of the complete set of featured articles, with the network mechanisms of a contrasting sample of comparable non-featured articles in the English-language edition of Wikipedia. Estimates of relational event models suggest that contributors to featured articles display greater deference toward the reputation of their team members. Contributors to featured articles also display a weaker tendency to follow the behavioral norms predicted by the theory of structural balance, and hence a weaker tendency toward polarization.
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ItemA Dynamic Sequence Model of Information Sharing Processes in Virtual Teams( 2019-01-08)Sharing information is a critical component of virtual team functioning. While prior research has identified the motivations for and the structure of information sharing, there has been little emphasis on the dynamic patterning of sharing behavior. In this study, we focus on the process of information sharing, namely the sequence and timing of individual decisions during a virtual team task. Further, we argue that sharing behaviors can be categorized into a finite number of approaches. We propose a temporal, event-based model to uncover the behavioral and cognitive factors that influence information sharing. With a sample of 600 participants organized into thirty ad hoc virtual teams, we demonstrate significant heterogeneity in sharing propensities. Our study makes two contributions to the extant literature. First, we extend theories regarding the motivation and structure of information sharing. Second, we make a broader methodological contribution with the application of a latent-class relational event model.
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