@inproceedings{zhao-etal-2025-scale,
title = "{SCALE}: Towards Collaborative Content Analysis in Social Science with Large Language Model Agents and Human Intervention",
author = "Zhao, Chengshuai and
Tan, Zhen and
Wong, Chau-Wai and
Zhao, Xinyan and
Chen, Tianlong and
Liu, Huan",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.416/",
doi = "10.18653/v1/2025.acl-long.416",
pages = "8473--8503",
ISBN = "979-8-89176-251-0",
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. Extensive evaluations on real-world datasets demonstrate that SCALE achieves human-approximated performance across various complex content analysis tasks, offering an innovative potential for future social science research."
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<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 \underlineSimulates \underlineContent \underlineAnalysis via \underlineLarge language model (LLM) ag\underlineEnts. 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. Extensive evaluations on real-world datasets demonstrate that SCALE achieves human-approximated performance across various complex content analysis tasks, offering an innovative potential for future social science research.</abstract>
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%0 Conference Proceedings
%T SCALE: Towards Collaborative Content Analysis in Social Science with Large Language Model Agents and Human Intervention
%A Zhao, Chengshuai
%A Tan, Zhen
%A Wong, Chau-Wai
%A Zhao, Xinyan
%A Chen, Tianlong
%A Liu, Huan
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F zhao-etal-2025-scale
%X 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 \underlineSimulates \underlineContent \underlineAnalysis via \underlineLarge language model (LLM) ag\underlineEnts. 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. Extensive evaluations on real-world datasets demonstrate that SCALE achieves human-approximated performance across various complex content analysis tasks, offering an innovative potential for future social science research.
%R 10.18653/v1/2025.acl-long.416
%U https://aclanthology.org/2025.acl-long.416/
%U https://doi.org/10.18653/v1/2025.acl-long.416
%P 8473-8503
Markdown (Informal)
[SCALE: Towards Collaborative Content Analysis in Social Science with Large Language Model Agents and Human Intervention](https://aclanthology.org/2025.acl-long.416/) (Zhao et al., ACL 2025)
ACL