Annotating Line Charts for Addressing Deception


연구 분야: Strategies



학회: CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems


초록

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.


Author Profile
Arlen Fan

School of Computing and Augmented Intelligence Arizona State University United States

Andorra
Author Profile
Yuxin Ma

Department of Computer Science and Engineering Southern University of Science and Technology China

Andorra
Author Profile
Michelle V Mancenido

School of Mathematical and Natural Sciences Arizona State Unversity United States

Andorra

📄 논문 정보

발행 연도 2022년
인용수 27
출판 국가 Andorra
사이트 ACM
좋아요 수 0

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