Proxona: Supporting Creators' Sensemaking and Ideation with LLM-Powered Audience Personas
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929719775068160 |
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| author | Choi, Yoonseo Kang, Eun Jeong Choi, Seulgi Lee, Min Kyung Kim, Juho |
| author_facet | Choi, Yoonseo Kang, Eun Jeong Choi, Seulgi Lee, Min Kyung Kim, Juho |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_10937 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Proxona: Supporting Creators' Sensemaking and Ideation with LLM-Powered Audience Personas Choi, Yoonseo Kang, Eun Jeong Choi, Seulgi Lee, Min Kyung Kim, Juho Human-Computer Interaction 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. |
| title | Proxona: Supporting Creators' Sensemaking and Ideation with LLM-Powered Audience Personas |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2408.10937 |