The Internet has fundamentally changed how work gets done in the 21st century. For example, people increasingly spend time on the Internet where they share and develop knowledge in online communities. Yet, little is known about how high-quality knowledge comes about in these communities. This is surprising, because stakeholders such as organizations, policy makers, or activist groups can profit from high-quality knowledge shared and produced in online communities. In this study, our goal is to test how social network structures influence the quality of knowledge developed in online communities. Our project will contribute to the literature on online communities and knowledge management, because we will conceptually develop and empirically test which social network structures are conducive for sharing and producing high-quality knowledge. As opposed to prior studies in this area, we will leverage automated techniques for our analysis. In particular, we will develop Natural Language Processing based techniques to recognize the substantial core and value of social media contributions. Besides the scientific contribution, our project will also deliver valuable insights for practitioners, which can aid in better managing online communities that are geared toward sharing and producing knowledge. We will use large data sets of three online communities to provide a ‘big data picture’. Our analysis will be based on a theory-based conceptualization of social network structures and knowledge quality. As a result of the automated analysis using the algorithms developed in the context of this project, we can automatically recognize high-quality knowledge based on a number of variables that are partly based on theory and partly developed using human coders. What is more, we can automatically extract the social network structures in the data. Based on these results, we will then test which social network structures are conducive for sharing and producing high-quality knowledge across the three data sets.
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