Words of estimative correlation : studying verbalizations of scatterplots

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Abstract

Natural language and visualization are being increasingly deployed together for supporting data analysis in different ways, from multimodal interaction to enriched data summaries and insights. Yet, researchers still lack systematic knowledge on how viewers verbalize their interpretations of visualizations, and how they interpret verbalizations of visualizations in such contexts. We describe two studies aimed at identifying characteristics of data and charts that are relevant in such tasks. The first study asks participants to verbalize what they see in scatterplots that depict various levels of correlations. The second study then asks participants to choose visualizations that match a given verbal description of correlation. We extract key concepts from responses, organize them in a taxonomy and analyze the categorized responses. We observe that participants use a wide range of vocabulary across all scatterplots, but particular concepts are preferred for higher levels of correlation. A comparison between the studies reveals the ambiguity of some of the concepts. We discuss how the results could inform the design of multimodal representations aligned with the data and analytical tasks, and present a research roadmap to deepen the understanding about visualizations and natural language.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Social Sciences > Centre for Interdisciplinary Methodologies
Library of Congress Subject Headings (LCSH): Information visualization, Natural language generation (Computer science) , Natural language processing (Computer science) , Human-computer interaction
Journal or Publication Title: IEEE Transactions on Visualization and Computer Graphics
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 1077-2626
Official Date: 1 April 2022
Dates:
Date
Event
1 April 2022
Published
11 September 2020
Available
31 August 2020
Accepted
Volume: 28
Number: 4
Page Range: pp. 1967-1981
DOI: 10.1109/TVCG.2020.3023537
Status: Peer Reviewed
Publication Status: Published
Re-use Statement: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 29 September 2020
Date of first compliant Open Access: 2 October 2020
RIOXX Funder/Project Grant:
Project/Grant ID
RIOXX Funder Name
Funder ID
EP/P025501/1
[EPSRC] Engineering and Physical Sciences Research Council
URI: https://wrap.warwick.ac.uk/142297/

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