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A month-long quasi-experiment was conducted using a distributed team responsible for modeling, simulation, and analysis. Six experiments of three different time durations (short, medium, and long) were performed. The primary goal was to discover if synchronous collaboration capability through a particular application improved the ability of the team to form a common mental model of the analysis problem(s) and solution(s). The results indicated that such collaboration capability did improve the formation of common mental models, both in terms of time and quality (i.e., depth of understanding), and that the improvement did not vary by time duration. In addition, common mental models were generally formed by interaction around a shared graphical image, the progress of collaboration was not linear but episodic, and tasks that required drawing and conversing at the same time were difficult to do.

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In this note we introduce a new methodology that combines tools from social language processing and network analysis to identify socially situated relationships between individuals, even when these relationships are latent or unrecognized. We call this approach social language network analysis (SLNA). We describe the philosophical antecedents of SLNA, the mechanics of preprocessing, processing, and post-processing stages, and the results of applying this approach to a 15-month corporate discussion archive. These example results include an explicit mapping of both the perceived expertise hierarchy and the social support / friendship network within this group.