Proxona: Leveraging LLM-Driven Personas to Enhance Creators' Understanding of Their Audience
<|reference_start|>Proxona: Leveraging LLM-Driven Personas to Enhance Creators' Understanding of Their Audience: Creators are nothing without their audience, and thereby understanding their audience is the cornerstone of their professional achievement. Yet many creators feel lost while comprehending audiences with existing tools, which offer insufficient insights for tailoring content to audience needs. To address the challenges creators face in understanding their audience, we present Proxona, a system for defining and extracting representative audience personas from the comments. Creators converse with personas to gain insights into their preferences and engagement, solicit feedback, and implement evidence-based improvements to their content. Powered by large language models, Proxona analyzes audience comments, distilling the latent characteristics of audiences into tangible dimensions (classification categories) and values (category attributes). Proxona then clusters these into synthetic personas. Our technical evaluations demonstrated that our pipelines effectively generated relevant and distinct dimensions and values, enabling the deduction of audience-reflecting personas, while minimizing the likelihood of hallucinations in persona responses. Our user evaluation with 11 creators showed that Proxona supported creators to gain new insights about their audience, make informed decisions, and successfully complete content creation with high confidence. Proxona's data-driven audience personas empower creators to seamlessly integrate audience perspectives into their creative processes, fostering a collaborative approach to content creation.<|reference_end|>
@article{choi2024proxona:,
    title={Proxona: Leveraging LLM-Driven Personas to Enhance Creators'
  Understanding of Their Audience},
    author={Yoonseo Choi, Eun Jeong Kang, Seulgi Choi, Min Kyung Lee and Juho Kim},
    journal={arXiv preprint arXiv:2408.10937},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10937},
    primaryClass={cs.HC}
}