Purchase this article with an account.
Sungjin Nam, Zoya Bylinskii, Christopher Tensmeyer, Rajiv Jain, Curtis Wigington, Tong Sun; Why are you reading this? Predicting reading goal and familiarity from people’s mobile interaction behaviors. Journal of Vision 2020;20(11):951. doi: https://doi.org/10.1167/jov.20.11.951.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
People read for different reasons and in different contexts. The reading behavior of a paralegal skimming a contract for specific legal phrases will look quite different than that of a student learning about unfamiliar concepts from a textbook. While the reading of printed material is still commonplace, reading in a mobile context is becoming increasingly popular. From an experimental perspective, mobile devices provide an effective platform for capturing behavioral data outside the laboratory, non-invasively and at scale. In this work, we study reading on mobile devices while measuring readers’ attention and behavior without the use of equipment like eye trackers. Specifically, we measure the effects of reading goal and familiarity on the mobile interaction behaviors (touch, scroll, reading time) of 285 crowdsourced (Mechanical Turk) participants reading approximately 500-word articles on mobile devices. We manipulated the familiarity condition by exposing participants to article summaries during a training phase, either related or unrelated to articles that they read during the main study. We manipulated the reading goal by changing instructions participants received, to encourage either ‘literal’ or ‘contextual’ reading. Our findings suggest that features based on touch locations can be used to distinguish between familiarity conditions, while scroll-based features and reading time can be used to differentiate reading goal conditions. We also built statistical models that, for a given participant, predict the familiarity level (55.6%) and reading goal (70.2%) with greater accuracy than a baseline model (51.1% (n.s.) and 51.7% (p<0.001), respectively). Moreover, individual differences in scrolling behaviors on a mobile device during reading affect the prediction of reading goals when using scrolling speed and idle time as features. The results of our studies motivate promising future investigations of reading on mobile, towards building predictive models of individual readers, as building blocks for customized reading experiences.
This PDF is available to Subscribers Only