ThemeDelta: Dynamic Segmentations over Temporal Topic Models.

We present ThemeDelta, a visual analytics system for extracting and visualizing temporal trends, clustering, and reorganization in time-indexed textual datasets. ThemeDelta is supported by a dynamic temporal segmentation algorithm that integrates with topic modeling algorithms to identify change points where significant shifts in topics occur. This algorithm detects not only the clustering and associations of keywords in a time period, but also their convergence into topics (groups of keywords) that may later diverge into new groups. The visual representation of ThemeDelta uses sinuous, variable-width lines to show this evolution on a timeline, utilizing color for categories, and line width for keyword strength. We demonstrate how interaction with ThemeDelta helps capture the rise and fall of topics by analyzing archives of historical newspapers, of U.S. presidential campaign speeches, and of social messages collected through iNeighbors, a web-based social website. ThemeDelta is evaluated using a qualitative expert user study involving three researchers from rhetoric and history using the historical newspapers corpus.

Understanding the Role of Medical Experts during a Public Health Crisis Digital Tools and Library Resources for Research on the 1918 Spanish Influenza.

Humanities scholars, particularly historians of health and disease, can benefit from digitized library collections and tools such as topic modeling. Using a case study from the 1918 Spanish Flu epidemic, this paper explores the application of a big humanities approach to understanding the impact of a public health official on the course of the disease and the response of the public, as documented through digitized newspapers and medical periodicals.