Annotating Line Charts for Addressing Deception

Arlen Fan, Yuxin Ma, Michelle Mancenido, Ross MacIejewski

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Deceptive visualizations are visualizations that, whether intentionally or not, lead the reader to an understanding of the data which varies from the actual data. Examples of deceptive visualizations can be found in every digital platform, and, despite their widespread use in the wild, there have been limited efforts to alert laypersons to common deceptive visualization practices. In this paper, we present a tool for annotating line charts in the wild that reads line chart images and outputs text and visual annotations to assess the line charts for distortions and help guide the reader towards an honest understanding of the chart data. We demonstrate the usefulness of our tool through a series of case studies on real-world charts. Finally, we perform a crowdsourced experiment to evaluate the ability of the proposed tool to educate readers about potentially deceptive visualization practices.

Original languageEnglish (US)
Title of host publicationCHI 2022 - Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450391573
DOIs
StatePublished - Apr 29 2022
Externally publishedYes
Event2022 CHI Conference on Human Factors in Computing Systems, CHI 2022 - Virtual, Online, United States
Duration: Apr 30 2022May 5 2022

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2022 CHI Conference on Human Factors in Computing Systems, CHI 2022
Country/TerritoryUnited States
CityVirtual, Online
Period4/30/225/5/22

Keywords

  • deceptive visualization
  • educational tools
  • visual literacy

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design
  • Software

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