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

TL;DR

This paper presents Co-Persona, a framework combining large language models and expert validation to analyze social media data for understanding user personas, demonstrated through a case study on bedside lamp preferences from millions of posts.

Contribution

It introduces a novel methodology integrating NLP and expert validation for large-scale social media persona analysis, with practical applications in marketing and product development.

Findings

Identified five distinct user personas based on bedtime behaviors.

Demonstrated the effectiveness of Co-Persona in processing large social media datasets.

Provided a structured approach for targeted marketing strategies.

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.