mlsanityData to 7 Oct 2026

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

arXiv 2408.10937 ↗First posted 20 Aug 2024cs.HCcited by 19

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Abstract (arXiv)

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.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Aug 2024.

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Citing papers per year: 2024: 1; 2025: 8; 2026: 10.

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  1. 2025Towards a Design Guideline for RPA Evaluation: A Survey of Large Language Model-Based Role-Playing Agentscited by 22
  2. 2025Boosting Relational Deep Learning with Pretrained Tabular Modelscited by 9
  3. 2025Surfacing Variations to Calibrate Perceived Reliability of MLLM-generated Image Descriptionscited by 6
  4. 2025Constella: Supporting Storywriters' Interconnected Character Creation through LLM-based Multi-Agentscited by 4
  5. 2025FeedQUAC: Quick Unobtrusive AI-Generated Commentarycited by 2
  6. 2025DIF: A Framework for Benchmarking and Verifying Implicit Bias in LLMscited by 2
  7. 2024Examining the Role of Relationship Alignment in Large Language Modelscited by 1
  8. 2025Free Lunch for User Experience: Crowdsourcing Agents for Scalable User Studiescited by 1
  9. 2026How to Model AI Agents as Personas?: Applying the Persona Ecosystem Playground to 41,300 Posts on Moltbook for Behavioral Insightscited by 1
  10. 2026Can You Make It Sound Like You? Post-Editing LLM-Generated Text for Personal Stylecited by 1
  11. 2026Belief-Guided Inference Control for Large Language Model Services via Verifiable Observationscited by 1
  12. 2025"She was useful, but a bit too optimistic": Augmenting Design with Interactive Virtual Personascited by 0
7 more
  1. 2026Improving Implicit Hate Speech Detection via a Community-Driven Multi-Agent Frameworkcited by 0
  2. 2026Personagram: Bridging Personas and Product Design for Creative Ideation with Multimodal LLMscited by 0
  3. 2026PACIFIC: Can LLMs Discern the Psychometric Traits Influencing Your Preferences? Personality-Driven Preference Alignment in LLMscited by 0
  4. 2026SocialFiVis: A Visual Analytics Sandbox for LLM-Grounded Multi-Agent Simulation in Social Financecited by 0
  5. 2026Proxy-Validated LLM UX Micro-Simulations: An Artifact-First Protocol for Early-Stage Decision Supportcited by 0
  6. 2026From Style Replication to Style Exploration: Enabling Art Style Exploration with Analyze-Experiment-Resituate Frameworkcited by 0
  7. 2026DocuTeam: Mixed-Initiative Multi-Agent Discussions around Evolving Documentscited by 0