Log-Based Reading Speed Prediction: A Case Study on War and Peace
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.
KeywordsReading speed Text difficulty Reading behavior User modeling
We thank Bookmate for granting access to the dataset.
- 1.Biedert, R., Hees, J., Dengel, A., Buscher, G.: A robust realtime reading-skimming classifier. In: Proceedings of the Symposium on Eye Tracking Research and Applications, pp. 123–130 (2012)Google Scholar
- 2.Bonch-Osmolovskaya, A., Skorinkin, D.: Text mining War and Peace: automatic extraction of character traits from literary pieces. Digit. Sch. Hum. 32(suppl\(\_\)1), i17–i24 (2016)Google Scholar
- 5.Chmykhova, E., Davydov, D., Lavrova, T.: Experimental study of factors of speed reading (Eksperimental’noe issledovanie faktorov skorosti chteniya). Russ. Psyhologiya Obucheniya 9, 26–36 (2014)Google Scholar
- 6.Constantinides, M., Dowell, J., Johnson, D., Malacria, S.: Exploring mobile news reading interactions for news app personalisation. In: Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services, pp. 457–462 (2015)Google Scholar
- 7.DuBay, W.H.: The principles of readability (2004). http://www.impact-information.com/impactinfo/readability02.pdf
- 8.Kunze, K., et al.: Quantifying reading habits: counting how many words you read. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 87–96 (2015)Google Scholar
- 9.Lagun, D., Lalmas, M.: Understanding user attention and engagement in online news reading. In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, pp. 113–122 (2016)Google Scholar
- 11.Manguel, A.: A History of Reading. Knopf Canada, New York (1996)Google Scholar
- 14.Pitler, E., Nenkova, A.: Revisiting readability: a unified framework for predicting text quality. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 186–195 (2008)Google Scholar