VisRecall: Quantifying Information Visualisation Recallability via Question Answering

TL;DR

This paper introduces VisRecall, a dataset and computational method for quantitatively assessing the recallability of information visualisations through a question-answering approach, aiding visualization design optimization.

Contribution

It presents the first dataset with human recallability scores for visualisations and a novel method to predict recallability of visualisation elements.

Findings

The method outperforms baselines in predicting overall and specific question type recallability.

VisRecall dataset contains 200 visualisations with crowd-sourced recallability annotations.

The approach enables quantitative assessment of visualisation effectiveness.

Abstract

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.