  Despite its importance for assessing the effectiveness of communicating
information visually, fine-grained recallability of information visualisations
has not been studied quantitatively so far. In this work, we propose a
question-answering paradigm to study visualisation recallability and present
VisRecall - a novel dataset consisting of 200 visualisations that are annotated
with crowd-sourced human (N = 305) recallability scores obtained from 1,000
questions of five question types. Furthermore, we present the first
computational method to predict recallability of different visualisation
elements, such as the title or specific data values. We report detailed
analyses of our method on VisRecall and demonstrate that it outperforms several
baselines in overall recallability and FE-, F-, RV-, and U-question
recallability. Our work makes fundamental contributions towards a new
generation of methods to assist designers in optimising visualisations.
