Research & Papers

Study reveals 3 emotion functions in affective visualization design

Interviews with 15 practitioners uncover how data designers intentionally craft emotional impact.

Deep Dive

A new study from researchers at multiple institutions — Yixin Bai, Ziyi Wang, Keke Wu, and Fumeng Yang — explores how visualization practitioners deliberately integrate emotions into their work, an area previously under-examined. Through semi-structured interviews with 15 professional designers and hybrid thematic analysis, the paper categorizes three primary functions emotions serve for viewers: entry (capturing attention), engagement (sustaining interest), and outcome (shaping interpretation or action). The designers work across three facets — data (e.g., choosing emotive datasets), design (e.g., color, typography, motion), and audience (e.g., tailoring emotional tone to user context) — each with specific strategies.

Perhaps the most striking finding is that emotional intent is rarely planned from the start; instead, it emerges organically as designers iterate. Emotional impact results from an accumulation of small design choices rather than a single visual element. The paper also highlights ethical considerations, such as the risk of manipulation or misrepresentation, and notes that evaluation remains a key challenge — designers struggle to measure whether viewers actually feel the intended emotions. Accepted at IEEE VIS 2026 as a short paper, this work provides a structured framework that can help both practitioners and researchers think more systematically about affective visualization design.

Key Points
  • Identifies three emotion functions: entry (capture attention), engagement (sustain interest), outcome (shape interpretation).
  • Designers work across data, design, and audience facets; emotional intent often emerges during iteration, not upfront.
  • Ethical concerns include manipulation risk; evaluation of emotional impact remains a major unsolved challenge.

Why It Matters

A structured framework for emotional design in data visualization — critical for responsible, engaging data storytelling.

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