Predicting Text Readability from Scrolling Interactions

Sian Gooding, Yevgeni Berzak, Tony Mak, Matt Sharifi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Judging the readability of text has many important applications, for instance when performing text simplification or when sourcing reading material for language learners. In this paper, we present a 518 participant study which investigates how scrolling behaviour relates to the readability of English texts. We make our dataset publicly available and show that (1) there are statistically significant differences in the way readers interact with text depending on the text level, (2) such measures can be used to predict the readability of text, and (3) the background of a reader impacts their reading interactions and the factors contributing to text difficulty.

Original languageEnglish
Title of host publicationCoNLL 2021 - 25th Conference on Computational Natural Language Learning, Proceedings
EditorsArianna Bisazza, Omri Abend
Pages380-390
Number of pages11
ISBN (Electronic)9781955917056
StatePublished - 2021
Externally publishedYes
Event25th Conference on Computational Natural Language Learning, CoNLL 2021 - Virtual, Online
Duration: 10 Nov 202111 Nov 2021

Publication series

NameCoNLL 2021 - 25th Conference on Computational Natural Language Learning, Proceedings

Conference

Conference25th Conference on Computational Natural Language Learning, CoNLL 2021
CityVirtual, Online
Period10/11/2111/11/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Human-Computer Interaction
  • Linguistics and Language

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