Evaluating User Satisfaction with Typography Designs via Mining Touch Interaction Data in Mobile Reading

Previous work has demonstrated that typography design has a great influence on users' reading experience. However, current typography design guidelines are mainly for general purpose, while the individual needs are nearly ignored. To achieve personalized typography designs, an important and necessary step is accurately evaluating user satisfaction with the typography designs. Current evaluation approaches, e.g., asking for users' opinions directly, however, interrupt the reading and affect users' judgments. In this paper, we propose a novel method to address this challenge by mining users' implicit feedbacks, e.g., touch interaction data. We conduct two mobile reading studies in Chinese to collect the touch interaction data from 91 participants. We propose various features based on our three hypotheses to capture meaningful patterns in the touch behaviors. The experiment results show the effectiveness of our evaluation models with higher accuracy on comparing with the baseline under three text difficulty levels, respectively.