A dataset search interface with LLM-generated query reformulations, semantic column and granularity filters, and task-specific relevance indicators that helped 12 study participants explore and make sense of dataset search results.
Tasks, Time, and Tools: Quantifying Online Sensemaking Efforts Through a Survey-based Study
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abstract
Aiming to help people conduct online research tasks, much research has gone into tools for searching for, collecting, organizing, and synthesizing online information. However, outside of the lab, in-the-wild sensemaking sessions (with data on tasks, users, their tools and challenges) can ground us in the reality of such efforts and the state of tool support. We use a survey-based approach with aided recall focused on segmenting and contextualizing individual exploratory browsing sessions to conduct a mixed method analysis of everyday sensemaking sessions in the traditional desktop browser setting while preserving user privacy. We report data from our survey (n=111) collected in September, 2022, and use these results to update and deepen the rich literature on information seeking behavior and exploratory search, contributing new empirical insights into the time spent per week and distribution of that time across tasks, and the lack of externalization and tool-use despite widespread desire for support.
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Rethinking Dataset Discovery with DataScout
A dataset search interface with LLM-generated query reformulations, semantic column and granularity filters, and task-specific relevance indicators that helped 12 study participants explore and make sense of dataset search results.