Using Behavioral Interactions from a Mobile Device to Classify the Reader's Prior Familiarity and Goal Conditions
This work addresses the need for personalized reading support in mobile applications, though it is incremental as it builds on existing methods for behavioral analysis.
The study tackled the problem of classifying a reader's prior familiarity and reading goals using behavioral interactions from mobile devices, achieving 67.5% accuracy for goal prediction and 56.2% for familiarity prediction compared to baselines.
A student reads a textbook to learn a new topic; an attorney leafs through familiar legal documents. Each reader may have a different goal for, and prior knowledge of, their reading. A mobile context, which captures interaction behavior, can provide insights about these reading conditions. In this paper, we focus on understanding the different reading conditions of mobile readers, as such an understanding can facilitate the design of effective personalized features for supporting mobile reading. With this motivation in mind, we analyzed the reading behaviors of 285 Mechanical Turk participants who read articles on mobile devices with different familiarity and reading goal conditions. The data was collected non-invasively, only including behavioral interactions recorded from a mobile phone in a non-laboratory setting. Our findings suggest that features based on touch locations can be used to distinguish among familiarity conditions, while scroll-based features and reading time features can be used to differentiate between reading goal conditions. Using the collected data, we built a model that can predict the reading goal condition (67.5%) significantly more accurately than a baseline model. Our model also predicted the familiarity level (56.2%) marginally more accurately than the baseline. These findings can contribute to developing an evidence-based design of reading support features for mobile reading applications. Furthermore, our study methodology can be easily expanded to different real-world reading environments, leaving much potential for future investigations.