Making Data-Driven Articles more Accessible: An Active Preference Learning Approach to Data Fact Personalization

Data-driven news articles are widely used to communicate societal phenomena with concrete evidence. These articles are often accompanied by a visualization, helping readers to contextualize content. However, blind and low vision (BLV) individuals have limited access to visualizations, hindering a deep understanding of data. We explore the possibility of dynamically generating data facts (texts describing data patterns in a chart) for BLV individuals based on their preferences to aid the reading of such articles. We conduct a formative study to understand how they perceive system-generated data facts and the factors influencing their preferences. The results indicate the preferences are highly varied among individuals, and a simple preference elicitation alone induces noise. Based on the findings, we developed a method to personalize the data facts generation using an active learning approach. The evaluation studies demonstrate that our model converges effectively and provides more preferable sets of data facts than the baseline.