F²Stories: A Modular Framework for Multi-Objective Optimization of Storylines with a Focus on Fairness

Area of Science:

• Computer Science
• Data Visualization
• Human-Computer Interaction

Background:

• Storyline visualizations depict character interactions over time.
• Optimizing global readability metrics can create biases, making minority groups harder to follow.
• Existing methods struggle to balance group readability with narrative structure.

Purpose of the Study:

• To develop a framework for balanced storyline visualizations across different character groups.
• To address biases in readability for minority or less-represented groups.
• To offer flexible optimization modes for fairness, focus, and standard readability.

Main Methods:

• Introduced F2Stories, a modular framework for storyline visualization.
• Utilized Mixed Integer Linear Programming (MILP) for optimization.
• Implemented three modes: fairnessMode, focusMode, and standardMode.

Main Results:

• F2Stories enables fairer storyline visualizations, prioritizing specific groups when needed.
• The framework maintains narrative structure while improving readability across groups.
• Experimental analysis and case studies demonstrate effectiveness in real-world contexts.

Conclusions:

• F2Stories provides a novel, optimized approach to multi-group storyline visualization.
• The MILP-based method offers optimality guarantees and flexibility.
• This framework enhances the visual representation of complex character dynamics.