We are studying the role of productive talk in synchronous, technologically mediated math learning environments. This project is led by Gerry Stahl, and includes a partnership with Rutgers University.
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Mark McKelvey, data scientist at Alight Analytics, & I will be giving talks geared towards network and graph theory. Mark will be talking about applying network analysis to marketing channel optimization. He'll show us a practical example and talk some about the tools of the trade. My talk will focus on using network and computational linguistic analysis to understand leadership and participation in virtual organizations like GitHub projects and other online systems including Facebook and Twitter. Sean’s analysis examples will be in R, and data collection examples are in Python.
In this article we synthesize three years of social technologies research, including studies of Facebook, Twitter and GitHub, to present a a theory driven framework to guide future social scientific research using “Big Data”. We connect levels of analysis derived from empirical study of influence to the core, electronic trace data generated by social technologies. Specifically, we outline a relationship between social media technology platforms, individual goals for participation and emergent small groups to inform future research on influence in social technologies. We incorporate theory from small group literature, communities and networks of practice and media theory to explicate a contextual framework for measuring influence. In our discussion we propose three categories of social technology: Social media, distributed work and participatory mass media, presenting Facebook, GitHub and Twitter as exemplars of each respective category.