Scalable Computational Analysis of the Diffusion
of Information Technology Concepts


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In any technology field, there is always some set of core concepts that dominate the agenda for research and practice. As a field progresses that set evolves, with new concepts replacing old ones. This project will model the dynamic social system through which technological concepts come to be perceived and understood. The primary research question is: How do the actions and opinions of individual actors give rise to more globally accepted concepts in a technology field, and how do such micro-macro dynamics change over time? That question will be answered by using an iterative process in which computational analysis of text is used to populate a model of salient aspects of social dynamics. Interpretations based on that model will then be used to guide refinement and enrichment of the computational analysis. This "computationally-supported case study" process offers a promising new approach to building and testing theories for social science research. By coupling focused extraction and classification for high-volume multi-source data with a multi-concept computational analysis strategy the project will create a new middle ground between today's richly analyzed but narrowly focused case studies and the presently available scalable but relatively shallow techniques, such as citation analysis. The project will focus on Information Technology as an exemplar field, leveraging the broad and accessible discourse on that topic found in vast collections of formal and informal sources. Text analytic techniques from information retrieval and computational linguistics will be adapted to detect specific concepts, and to connect those concepts with the people who write about them and with attitudes that those people express. An early goal will be to explain the extent to which the popularity of concepts results from social actors' actions and opinions. Contributions of this research are expected to include a scalable analytical framework that can affordably be extended to a broad range of other technologies, and a deeper understanding of the types of leverage that can be obtained from emerging text analytic techniques to enable new approaches to social science research.

This project is supported by the National Science Foundation, Division of Information & Intelligent Systems, Award Number 0729459 under the Human and Social Dynamics program.

Last updated May 8, 2009