The effect of user characteristics in time series visualizations

Abstract

There is increasing evidence that user characteristics can have a significant impact on visualization effectiveness, suggesting that visualizations could be enriched with personalization mechanisms that better fit each user's specific needs and abilities. In this paper, we contribute to this body of work with a study that investigates the impact of six user characteristics on the effectiveness of time series visualizations, which was not previously investigated in relation to personalizing Information visualization. We report on a controlled user study that compare four possible time series visualization techniques. User performance and how it was affected by user characteristics was measured while performing tasks from a formal taxonomy using Twitter data about real-world events. Our results show that both the personality trait of locus of control and the cognitive ability of verbal working memory influence which visualization is more effective when dealing with demanding and complex tasks. These findings extend the need for personalization to visualizations for time series data, and we discuss them in the context of creating systems that can utilize knowledge of the user's specific characteristics in order to present the most suitable visualization for each user.