Co-persona: Leveraging LLMs and Expert Collaboration to Understand User Personas through Social Media Data Analysis

This study introduces Co-Persona, a methodological framework bridging large-scale social media analysis with authentic user understanding through systematic integration of Large Language Models and expert validation. Through a case study of B.Co, a Chinese manufacturer, we investigated Co-Persona application in bedside lamp development. Our methodology analyzed over 38 million posts from Xiao Hongshu, employing multi-stage data processing combining advanced NLP with expert validation. Analysis revealed five user personas derived from bedtime behaviors: Health Aficionados, Night Owls, Interior Decorators, Child-care Workers, and Workaholics-each showing unique pre-sleep activities and product preferences. Findings demonstrate Co-Persona enhances manufacturers' ability to process large datasets while maintaining user understanding. The methodology provides structured approaches for targeted marketing and product strategies. Research contributes to theoretical understanding of data-driven persona development and practical applications in consumer-driven innovation. Code and data available at https://github.com/INFPa/LLMwithPersona.

Publication Details

Journal
ArXiv.org
Published
2025-06-23
Primary Topic
Persona Design and Applications
Type
article
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article

Co-persona: Leveraging LLMs and Expert Collaboration to Understand User Personas through Social Media Data Analysis

ArXiv.org
Persona Design and Applications
article

Co-persona: Leveraging LLMs and Expert Collaboration to Understand User Personas through Social Media Data Analysis

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Abstract

This study introduces Co-Persona, a methodological framework bridging large-scale social media analysis with authentic user understanding through systematic integration of Large Language Models and expert validation. Through a case study of B.Co, a Chinese manufacturer, we investigated Co-Persona application in bedside lamp development. Our methodology analyzed over 38 million posts from Xiao Hongshu, employing multi-stage data processing combining advanced NLP with expert validation. Analysis revealed five user personas derived from bedtime behaviors: Health Aficionados, Night Owls, Interior Decorators, Child-care Workers, and Workaholics-each showing unique pre-sleep activities and product preferences. Findings demonstrate Co-Persona enhances manufacturers' ability to process large datasets while maintaining user understanding. The methodology provides structured approaches for targeted marketing and product strategies. Research contributes to theoretical understanding of data-driven persona development and practical applications in consumer-driven innovation. Code and data available at https://github.com/INFPa/LLMwithPersona.

ArXiv.org
Industry, innovation and infrastructure
Openalex Percentile: Top 47%
Persona Design and Applications
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Co-persona: Leveraging LLMs and Expert Collaboration to Understand User Personas through Social Media Data Analysis · ArXiv.org (2025) | TGRS Research Map | TGRS