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Tasks, Time, and Tools: Quantifying Online Sensemaking Efforts Through a Survey-based Study

Andrew Kuznetsov, Michael Xieyang Liu, Aniket Kittur

TL;DR

This work uses 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.

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.

Tasks, Time, and Tools: Quantifying Online Sensemaking Efforts Through a Survey-based Study

TL;DR

This work uses 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.

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.

Paper Structure

This paper contains 21 sections, 14 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Characterizing Everyday Online Research
  4. Sensemaking in Online Research Tasks
  5. Augmenting Online Sensemaking
  6. Sensemaking Survey
  7. Survey Design
  8. Participants
  9. Validity Checks
  10. Survey Results
  11. How much time do people spend on sensemaking efforts on the web?
  12. Overview of Task Types
  13. Forces that sustain sensemaking efforts
  14. Forces that deescalate sensemaking efforts
  15. Tool Usage
  16. ...and 6 more sections

Figures (14)

  • Figure 1: An overview of this study's survey method: a pre-collection phase, followed by a two-part collection of online research tasks, followed by a post-collection phase. A high level overview of the questions in each of the phases is enumerated in the grey boxes to the right of each.
  • Figure 2: A violin plot of the sensemaking sessions by topic, sorted from top to bottom by average number of tasks in the dataset. Vertical gray bars within each plot indicate quartiles, a '+' symbol within each plot represents the average session duration. Individual sessions are plotted using a strip plot with transparent circles designating each session. Task categories below 1% are represented at the bottom in the 'Long Tail Tasks' plot. Further below, a histogram of session durations guides the reader in understanding the distribution of the data.
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  • ...and 9 more figures