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.
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Notes
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We were unable to test this hypothesis due to incomplete data.
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To calculate coverage we summed up all character ranges in the log entries for a particular reader. Coverage above 100% occurs, when the same text spans are read or just flipped through several times.
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We thank Bookmate for granting access to the dataset.
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Tukh, I., Braslavski, P., Buraya, K. (2019). Log-Based Reading Speed Prediction: A Case Study on War and Peace. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2019. Lecture Notes in Computer Science(), vol 11832. Springer, Cham. https://doi.org/10.1007/978-3-030-37334-4_11
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