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Log-Based Reading Speed Prediction: A Case Study on War and Peace

  • Igor Tukh
  • Pavel Braslavski
  • Kseniya BurayaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11832)

Abstract

In this exploratory study, we analyze reading behavior using logs from an ebook reading app. The logs contain users’ page turns along with time stamps and page sizes in characters. We focus on 17 readers of War and Peace by Leo Tolstoy, who read at least 80% of the novel. We aim at learning a regression model for reading speed based on shallow textual (e.g. word and sentence lengths) and contextual (e.g. time of the day and position in the book) features. Contextual features outperform textual ones and allow to predict reading speed with moderate quality. We share insights about the results and outline directions for future research. The analysis of reading behavior can be beneficial for school education, reading promotion, book recommendation, as well as for traditional creative writing and interactive fiction design.

Keywords

Reading speed Text difficulty Reading behavior User modeling 

Notes

Acknowledgements

We thank Bookmate for granting access to the dataset.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Higher School of EconomicsSaint PetersburgRussia
  2. 2.Ural Federal UniversityYekaterinburgRussia
  3. 3.JetBrains ResearchSaint PetersburgRussia
  4. 4.ITMO UniversitySaint PetersburgRussia

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