Predicting Text Readability from Scrolling Interactions
This work addresses text readability prediction for applications like text simplification and language learning, but it is incremental as it builds on existing readability research by adding scrolling data.
The study tackled the problem of predicting text readability by analyzing scrolling interactions from 518 participants, showing that scrolling behavior can be used to predict readability and that reader background affects these interactions.
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 a text. 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.