{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/scale-towards-collaborative-content-analysis","title":"SCALE: Towards Collaborative Content Analysis in Social Science with Large Language Model Agents and Human Intervention","arxiv_id":"2502.10937","date":"2025-02-16","proceeding":null,"authors":["Chengshuai Zhao","Zhen Tan","Chau-Wai Wong","Xinyan Zhao","Tianlong Chen","Huan Liu"],"abstract":"Content analysis breaks down complex and unstructured texts into theory-informed numerical categories. Particularly, in social science, this process usually relies on multiple rounds of manual annotation, domain expert discussion, and rule-based refinement. In this paper, we introduce SCALE, a novel multi-agent framework that effectively $\\underline{\\textbf{S}}$imulates $\\underline{\\textbf{C}}$ontent $\\underline{\\textbf{A}}$nalysis via $\\underline{\\textbf{L}}$arge language model (LLM) ag$\\underline{\\textbf{E}}$nts. SCALE imitates key phases of content analysis, including text coding, collaborative discussion, and dynamic codebook evolution, capturing the reflective depth and adaptive discussions of human researchers. Furthermore, by integrating diverse modes of human intervention, SCALE is augmented with expert input to further enhance its performance. 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