# Navigating the Risks of Using Large Language Models for Text Annotation in Social Science Research

arXiv: https://arxiv.org/abs/2503.22040 · HTML: https://mlsanity.com/paper/2503.22040/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 64 references that are in the map.
- First posted on arXiv 27 Mar 2025.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback](https://mlsanity.com/paper/2305.14975/) (2023)
- [How is ChatGPT's behavior changing over time?](https://mlsanity.com/paper/2307.09009/) (2023)
- [Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes](https://mlsanity.com/paper/1711.08412/) (2017)
- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
- [Better Zero-Shot Reasoning with Role-Play Prompting](https://mlsanity.com/paper/2308.07702/) (2023)
- [Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters](https://mlsanity.com/paper/2212.10001/) (2022)
- [Large Language Model Guided Tree-of-Thought](https://mlsanity.com/paper/2305.08291/) (2023)
- [Demystifying Prompts in Language Models via Perplexity Estimation](https://mlsanity.com/paper/2212.04037/) (2022)
- [Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks](https://mlsanity.com/paper/2306.07899/) (2023)
- [The Geometry of Culture: Analyzing Meaning through Word Embeddings](https://mlsanity.com/paper/1803.09288/) (2018)
- [Toxicity Detection with Generative Prompt-based Inference](https://mlsanity.com/paper/2205.12390/) (2022)
- [Prompt Stability Scoring for Text Annotation with Large Language Models](https://mlsanity.com/paper/2407.02039/) (2024)
- [Improving Large Language Models for Clinical Named Entity Recognition via Prompt Engineering](https://mlsanity.com/paper/2303.16416/) (2023)
- [Introduction to Neural Transfer Learning with Transformers for Social Science Text Analysis](https://mlsanity.com/paper/2102.02111/) (2021)
- [Theoretical foundations and limits of word embeddings: what types of meaning can they capture?](https://mlsanity.com/paper/2107.10413/) (2021)

## Built on by (most cited first, 4 of 4)

- [Vibe Researching as Wolf Coming: Can AI Agents with Skills Replace or Augment Social Scientists?](https://mlsanity.com/paper/2602.22401/) (2026)
- [Generative AI in Sociological Research: State of the Discipline](https://mlsanity.com/paper/2511.16884/) (2025)
- [Large Language Model-Informed Feature Discovery Improves Prediction and Interpretation of Credibility Perceptions of Visual Content](https://mlsanity.com/paper/2504.10878/) (2025)
- [Can Large Language Models Reliably Code Qualitative Humanitarian Data? A Benchmark Study Against Human Expert Adjudication](https://mlsanity.com/paper/2606.26541/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have the potential to revolutionize computational social science, particularly in automated textual analysis. In this paper, we conduct a systematic evaluation of the promises and risks associated with using LLMs for text classification tasks, using social movement studies as an example. We propose a framework for social scientists to incorporate LLMs into text annotation, either as the primary coding decision-maker or as a coding assistant. This framework offers researchers tools to develop the potential best-performing prompt, and to systematically examine and report the validity and reliability of LLMs as a methodological tool. Additionally, we evaluate and discuss its epistemic risks associated with validity, reliability, replicability, and transparency. We conclude with several practical guidelines for using LLMs in text annotation tasks and offer recommendations for more effectively communicating epistemic risks in research.