Digital social networks generate massive amounts of data that are sometimes tagged by their users. In this work we have developed an analytic pipeline to extract and visualize a hierarchical representation of the data for navigation and aggregation purposes.For this thesis, we gathered a collection of Q&A forums containing millions of tagged ques- tions. Our objective is to create a navigable structure from the data that provides a con- tinuum of views from the big picture, down to the details.We also devised a new algorithm to prune the data while preserving the important parts and relationships among data for very large datasets. We also showed a recursive aggregation approach to generate labels and timelines for intermediate nodes in the hierarchy.
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Thesis advisor: Shaw, Chris
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