  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.
