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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.09638 |
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| _version_ | 1866916054854270976 |
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| author | Fang, Qixiang Bernardo, Javier Garcia van Kesteren, Erik-Jan |
| author_facet | Fang, Qixiang Bernardo, Javier Garcia van Kesteren, Erik-Jan |
| contents | Large language models (LLMs) are increasingly used by researchers in the social sciences and humanities (SSH) for text analysis, particularly to automate text annotation. However, many researchers still face challenges in adopting LLMs, addressing their limitations, and producing reproducible workflows and results. For example, annotation errors can bias downstream statistical analyses even when apparent accuracy is high. This paper provides a step-by-step methodological guide to using LLMs for text annotation in SSH research, with practical Python and R examples. We explain how LLMs work, how to set up research projects, how to interact with (open-source) LLMs programmatically, how to design and evaluate prompts without overfitting, how to integrate LLM annotations into statistical analyses while accounting for annotation error, and how to manage cost, efficiency, and reproducibility at scale. Throughout, we emphasize intuitive methodological reasoning, concrete examples, and best practices to help researchers incorporate LLM-based annotation into reproducible scientific workflows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_09638 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Methodological Guide on Using Large Language Models for Reproducible Text Annotation in the Social Sciences and Humanities with Python and R Fang, Qixiang Bernardo, Javier Garcia van Kesteren, Erik-Jan Computers and Society Large language models (LLMs) are increasingly used by researchers in the social sciences and humanities (SSH) for text analysis, particularly to automate text annotation. However, many researchers still face challenges in adopting LLMs, addressing their limitations, and producing reproducible workflows and results. For example, annotation errors can bias downstream statistical analyses even when apparent accuracy is high. This paper provides a step-by-step methodological guide to using LLMs for text annotation in SSH research, with practical Python and R examples. We explain how LLMs work, how to set up research projects, how to interact with (open-source) LLMs programmatically, how to design and evaluate prompts without overfitting, how to integrate LLM annotations into statistical analyses while accounting for annotation error, and how to manage cost, efficiency, and reproducibility at scale. Throughout, we emphasize intuitive methodological reasoning, concrete examples, and best practices to help researchers incorporate LLM-based annotation into reproducible scientific workflows. |
| title | A Methodological Guide on Using Large Language Models for Reproducible Text Annotation in the Social Sciences and Humanities with Python and R |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2604.09638 |