Feedback beyond accuracy: Using eye-tracking to detect comprehensibility and interest during reading

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Knowing what information a user wants is a paramount challenge to information science and technology. Implicit feedback is key to solving this challenge, as it allows information systems to learn about a user's needs and preferences. The available feedback, however, tends to be limited and its interpretation shows to be difficult. To tackle this challenge, we present a user study that explores whether tracking the eyes can unpack part of the complexity inherent to relevance and relevance decisions. The eye behavior of 30 participants reading 18 news articles was compared with their subjectively appraised comprehensibility and interest at a discourse level. Using linear regression models, the eye-tracking signal explained 49.93% (comprehensibility) and 30.41% (interest) of variance (p <.001). We conclude that eye behavior provides implicit feedback beyond accuracy that enables new forms of adaptation and interaction support for personalized information systems.

Original languageEnglish
JournalJournal of the Association for Information Science and Technology
Issue number1
Pages (from-to)3-16
Number of pages14
Publication statusPublished - 2023

Bibliographical note

Funding Information:
The authors thank the anonymous reviewers, who provided valuable, detailed comments and suggestions on an earlier version of this paper. This enabled us to improve the paper substantially. Furthermore, the Dutch Organisation for Scientific Research (NWO) is gratefully acknowledged for funding the IPPSI‐KIEM project Adaptive Text‐Mining (ATM) (project number: 628.005.006), under which this work was conducted. 1

Publisher Copyright:
© 2022 The Authors. Journal of the Association for Information Science and Technology published by Wiley Periodicals LLC on behalf of Association for Information Science and Technology.

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