research-article

Investigating Searchers’ Mental Models to Inform Search Explanations

Authors:

Bodo Billerbeck,

Ryen W. WhiteAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 38, Issue 1

Article No.: 10, Pages 1 - 25

https://doi.org/10.1145/3371390

Published: 20 December 2019 Publication History

Abstract

Modern web search engines use many signals to select and rank results in response to queries. However, searchers’ mental models of search are relatively unsophisticated, hindering their ability to use search engines efficiently and effectively. Annotating results with more in-depth explanations could help, but search engine providers need to know what to explain. To this end, we report on a study of searchers’ mental models of web selection and ranking, with more than 400 respondents to an online survey and 11 face-to-face interviews. Participants volunteered a range of factors and showed good understanding of important concepts such as popularity, wording, and personalization. However, they showed little understanding of recency or diversity and incorrect ideas of payment for ranking. Where there are already explanatory annotations on the results page—such as “ad” markers and keyword highlighting—participants were familiar with ranking concepts. This suggests that further explanatory annotations may be useful.

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Index Terms

Investigating Searchers’ Mental Models to Inform Search Explanations
1. Information systems
  1. Information retrieval
    1. Users and interactive retrieval
  2. World Wide Web
    1. Web searching and information discovery
      1. Web search engines
        Page and site ranking

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 38, Issue 1

January 2020

301 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3368262

Editor:
Maarten de Rijke
University of Amsterdam, The Netherlands

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Publication History

Published: 20 December 2019

Accepted: 01 November 2019

Revised: 01 September 2019

Received: 01 June 2019

Published in TOIS Volume 38, Issue 1

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Shelby RRismani SRostamzadeh N(2024)Generative AI in Creative Practice: ML-Artist Folk Theories of T2I Use, Harm, and Harm-ReductionProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642461(1-17)Online publication date: 11-May-2024
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