Characteristics of deep and skim reading on smartphones vs. desktop: A comparative study

Xiuge Chen, Namrata Srivastava, Rajiv Jain, Jennifer Healey, Tilman Dingler

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2 Citations (Scopus)


Deep reading fosters text comprehension, memory, and critical thinking. The growing prevalance of digital reading on mobile interfaces raises concerns that deep reading is being replaced by skimming and sifting through information, but this is currently unmeasured. Traditionally, reading quality is assessed using comprehension tests, which require readers to explicitly answer a set of carefully composed questions. To quantify and understand reading behaviour in natural settings and at scale, however, implicit measures are needed of deep versus skim reading across desktop and mobile devices, the most prominent digital reading platforms. In this paper, we present an approach to systematically induce deep and skim reading and subsequently train classifiers to discriminate these two reading styles based on eye movement patterns and interaction data. Based on a user study with 29 participants, we created models that detect deep reading on both devices with up to 0.82 AUC. We present the characteristics of deep reading and discuss how our models can be used to measure the effect of reading UI design and monitor long-term changes in reading behaviours.

Original languageEnglish
Title of host publicationProceedings of the 2023 CHI Conference on Human Factors in Computing Systems
EditorsStephanie Mueller, Julie R. Williamson, Max L. Wilson
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages14
ISBN (Electronic)9781450394215
Publication statusPublished - 2023
EventInternational Conference on Human Factors in Computing Systems 2023 - , Germany
Duration: 23 Apr 202328 Apr 2023 (Website) (Proceedings)


ConferenceInternational Conference on Human Factors in Computing Systems 2023
Abbreviated titleCHI 2023
Internet address


  • Deep reading
  • Digital Devices
  • Eye tracking
  • Gaze features
  • Reading mode classification

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