Proxona: Supporting Creators' Sensemaking and Ideation with LLM-Powered Audience Personas

要旨

A content creator's success depends on understanding their audience, but existing tools fail to provide in-depth insights and actionable feedback necessary for effectively targeting their audience. We present Proxona, an LLM-powered system that transforms static audience comments into interactive, multi-dimensional personas, allowing creators to engage with them to gain insights, gather simulated feedback, and refine content. Proxona distills audience traits from comments, into dimensions (categories) and values (attributes), then clusters them into interactive personas representing audience segments. Technical evaluations show that Proxona generates diverse dimensions and values, enabling the creation of personas that sufficiently reflect the audience and support data grounded conversation. User evaluation with 11 creators confirmed that Proxona helped creators discover hidden audiences, gain persona-informed insights on early-stage content, and allowed them to confidently employ strategies when iteratively creating storylines. Proxona introduces a novel creator-audience interaction framework and fosters a persona-driven, co-creative process.

著者
Yoonseo Choi
KAIST, Daejeon, Korea, Republic of
Eun Jeong Kang
Cornell University, Ithaca, New York, United States
Seulgi Choi
KAIST, Daejeon, Korea, Republic of
Min Kyung Lee
University of Texas at Austin, Austin, Texas, United States
Juho Kim
KAIST, Daejeon, Korea, Republic of
DOI

10.1145/3706598.3714034

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714034

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Co-ideation

G314+G315
7 件の発表
2025-05-01 18:00:00
2025-05-01 19:30:00
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