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Untangling Rhetoric, Pathos, and Aesthetics in Data Visualization.

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    This summary is machine-generated.

    This study explores pathos (emotional appeal) in data visualization, examining its rhetorical and aesthetic functions. It offers a historical perspective to understand how emotional appeals integrate into data design.

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    Area of Science:

    • Data Visualization
    • Rhetoric
    • Aesthetics
    • Philosophy of Science

    Background:

    • Contemporary data communication primarily focuses on logos (reason) and ethos (credibility).
    • Pathos (emotional appeal) is increasingly recognized in data visualization but its links to rhetoric and aesthetics are underexplored.
    • Existing research lacks a historical perspective on these concepts within data visualization.

    Purpose of the Study:

    • To define and contextualize logos, ethos, and pathos within data visualization.
    • To explore the historical development and interrelations of these rhetorical concepts.
    • To illustrate pathos as a rhetorical strategy in contemporary data visualizations using Campbell's seven circumstances.

    Main Methods:

    • Historical analysis of rhetorical and philosophical concepts.
    • Development of working definitions for logos, ethos, and pathos in data visualization.
    • Application of Campbell's seven circumstances to analyze pathos in data visualization examples.

    Main Results:

    • Pathos functions as a significant rhetorical strategy in data visualization design.
    • The interplay between rhetorical strategies, aesthetic qualities, and emotional appeal is crucial for effective data communication.
    • A historical viewpoint enhances the understanding of integrating these elements in the design process.

    Conclusions:

    • Understanding pathos, alongside logos and ethos, provides a more holistic framework for data visualization.
    • Integrating rhetorical strategies and aesthetic considerations is key to leveraging emotional appeal effectively.
    • This research contributes to a deeper comprehension of the design process in data visualization.