Source-linked AI summary
Bringing Data to Life: Designing Data Characters for the Emotional Self
Diego Abarcar Calugay, Isabella Amador, Keke Wu
TL;DR
Long-term journal entries can be difficult to compare and interpret because conventional text visualizations often miss affective nuance. The paper introduces customizable human-like Data Characters as a design probe for representing affect through facial and bodily features, with preliminary walkthroughs indicating intuitive and feasible use. It contributes an exploratory approach to studying anthropomorphic visual encodings of affective experiences.
Problem
Accumulated journal entries are difficult to interpret and compare because existing text visualizations can overlook affective context and nuance.
Method
The authors use a customizable Data Character design probe that lets users represent affective experiences by manipulating facial features and body posture.
Results
Preliminary walkthroughs with two participants found the interface easy to use and its customization options capable of expressing personalized, nuanced emotion.
Takeaways & Limitations
Data Characters offer an exploratory way to study how affective experiences can be visually represented and encoded through anthropomorphic forms.
Takeaways & Limitations
Preliminary evidence comes from two participants representing one emotion, while nonverbal expressions may not generalize across individuals or cultures and the system omits broader emotional context.
Abstract
from arXiv · showhide
Journaling is a common practice for emotional expression, reflection, and processing. However, as entries accumulate, it can become difficult to interpret and compare their affective content, especially since traditional text-based analyses and visualizations often struggle to convey affective nuance. We introduce Data Characters, a visualization approach that represents affective content in journaling through human-like characters. Using a customizable Data Character as a design probe, we investigate the potential of character-based representations for conveying affective experiences and explore what visual encodings emerge through customization. Preliminary walkthroughs with two participants demonstrate the intuitiveness and feasibility of the approach. This work contributes an exploratory approach to studying how affective experiences can be visually represented and encoded through anthropomorphic forms.
1 INTRODUCTION
Journaling supports emotional reflection, but accumulated entries are difficult to interpret because conventional visualizations often miss affective context and nuance. Data Characters address this gap by representing journaling affect through customizable human-like expressions and postures.
- Accumulated journal entries make affective patterns, comparisons, and emotional changes difficult to identify.
- Word- and sentence-level visualizations can emphasize frequency while overlooking emotional context and nuance.
- Thematic coding can reduce complex affective experiences to discrete categories such as “happy” or “sad.”
- Data Characters represent journaling affect through human-like facial expressions and body postures.
- A customizable Data Character lets people construct personal affective representations while exposing character features as potential visual encodings.
2 DESIGN RATIONALE
Data Characters use anthropomorphic, full-body characters to represent affective experiences through familiar nonverbal cues. Their customizable features leave emotion-to-visual mappings open for users to establish.
- Facial expressions and body postures provide familiar nonverbal cues for communicating emotions.
- Prior visualization research suggests human-like features may support emotional expression and interpretation, although their effectiveness remains contested.
- Data Characters represent affective experiences through full-body, human-like characters rather than prescribing fixed mappings from emotions to features.
- The design asks how Data Characters can represent affective experiences and what visual encodings emerge through customization.
3 DATA CHARACTERS AS A DESIGN PROBE
The design probe lets users construct affective representations by adjusting facial and bodily features across continuous controls. In preliminary walkthroughs, two participants found the interface easy to use and expressive for personalized representations of anger.
- Users manipulate facial features and body posture to represent affective experiences without a prescribed feature mapping.
- The interface draws on continuous, multidimensional emotion models to support nuanced affective representations.
- Facial controls interpolate between expressions such as frown and smile, including mouth, eye, and eyebrow adjustments.
- Posture controls adjust limb openness and flexion, alongside head and body orientation and lean.
- Two participants found the interface easy to use and said its customization options supported personalized, nuanced expressions of anger.
4 IMPLICATIONS
Data Characters offer a way to visualize affective experiences in personal text data through familiar anthropomorphic cues. The approach opens a design space for studying direct affective representation and visual encoding.
- Data Characters provide an alternative to lexical, thematic, and statistical visualizations by treating affective experiences as visualizable content.
- Character-based designs use familiar anthropomorphic cues to represent affect rather than relying only on abstract visual forms.
- The approach may support interpretation of emotional patterns across time and between individuals, including in collaborative and therapeutic settings.
- Data Characters open a design space for direct affective representation and motivate further research into visual affect encoding.
5 LIMITATIONS & FUTURE WORK
The paper identifies several boundaries for Data Characters: limited preliminary evidence, uncertain generalizability, uneven intuitiveness, and incomplete representation of emotional context.
- Two participants represented one emotion, so the walkthroughs provide initial feasibility evidence rather than a systematic evaluation.
- Future work: Future work should examine broader affective experiences, cross-cultural interpretation, alternative representation techniques, and joint affective-contextual encoding.
- Nonverbal expressions may not be universal, limiting the generalizability of Data Character representations across individuals and cultures.
- Anthropomorphizing emotions may not be intuitive or expressive for everyone.
- The system focuses primarily on affect and does not represent the broader interactions, relationships, and events shaping emotional experiences.
6 CONCLUSION
The paper introduces Data Characters as an exploratory approach to representing journaling affect through customizable anthropomorphic forms and facial and bodily encodings.
- Data Characters represent affective content in journaling through customizable human-like characters.
- The design probe examines how facial and bodily features may serve as affective visual encodings.
- The work highlights anthropomorphism as a promising approach to visualizing affective and experiential data.
1 INTRODUCTION
Journaling supports emotional reflection, expression, and regulation, but long-term affective patterns can be difficult to interpret when visualizations omit context and nuance.
- Journaling supports emotional reflection, expression, and regulation.
- Interpreting long-term emotional patterns can be difficult because visualizations often fail to capture affective nuance.
- Word clouds focus on textual features rather than fully conveying affective experiences.
- Discrete themes reduce emotional content to separated categories, limiting nuanced insights.
2 DESIGN RATIONALE
The design rationale draws on familiar nonverbal and anthropomorphic cues to represent affective experiences with human-like visual forms.
- Figure 3 presents anthropographics as motivating the usefulness of human-like visuals.
- The approach draws on familiar facial features and body language as cues for emotional expression.
- Anthropomorphism assigns human traits to nonhuman entities and can support emotional expression in visualization.
- Prior work describes voices and faces as clarifying complex ideas and flexible human-posture displays as emotionally expressive.
- Data Characters are anthropomorphic representations of emotional content.
3 DESIGN PROBE
The design probe lets users construct affective Data Characters by modifying facial and bodily features. Sliders support continuous emotional representation, while drag-and-drop handles control limb movement.
- The customizable Data Character lets users express affective data by modifying facial and bodily features.
- Sliders provide continuous emotional representation.
- Drag-and-drop handles control limb movement.
- Users generated personalized visualizations of anger.
5 IMPLICATIONS
Data Characters are presented as alternatives to traditional visualizations that directly visualize affect and may support emotional communication. Their generalizability and contextual coverage remain limited, motivating broader studies and expanded representations.
- Data Characters directly visualize affect and may support emotional communication across domains such as psychotherapy.
- Nonverbal expressions may not be universal, limiting the generalizability of users’ designs.
- Data Characters may capture emotions without capturing the contextual details of rich affective experiences.
- The authors aim to conduct a larger design-probe study examining how Data Characters represent varied emotional experiences and common affective encoding strategies.
- Future work includes examining cultural differences, exploring visual metaphors, and representing affective and contextual information such as life events.