In this research, we aim to understand how data-driven techniques can automatically identify changes in user behavior (inflection points) based on user interaction logs collected from eye tracking and mouse interactions.
This paper explores the use of interaction logs to generate LDA based summaries of an analyst’s interest based on interactions with specific data items in a text analysis scenario.
We introduce ProvThreads, a visual analytics approach that incorporates interactive topic modeling outcomes to illustrate relationships between user interactions and the data topics under investigation.
Exploring how users use visualizations and switch between contexts.