A Human-Centered Workflow for Using Large Language Models in Content Analysis
arXiv:2603.19271v1 Announce Type: cross Abstract: While many researchers use Large Language Models (LLMs) through chat-based access, their real potential lies in leveraging LLMs via application programming interfaces (APIs). This paper conceptualizes LLMs as universal text processing machines and presents a comprehensive workflow for employing LLMs in three qualitative and quantitative content analysis tasks: (1) annotation (an umbrella term for qualitative coding, labeling and text classification), (2) summarization, and (3) information extraction. The workflow is explicitly human-centered. Researchers design, supervise, and validate each stage of the LLM process to ensure rigor and transparency. Our approach synthesizes insights from extensive methodological literature across multiple disciplines: political science, sociology, computer science, psychology, and management. We outline validation procedures and best practices to address key limitations of LLMs, such as their black-box
arXiv:2603.19271v1 Announce Type: cross Abstract: While many researchers use Large Language Models (LLMs) through chat-based access, their real potential lies in leveraging LLMs via application programming interfaces (APIs). This paper conceptualizes LLMs as universal text processing machines and presents a comprehensive workflow for employing LLMs in three qualitative and quantitative content analysis tasks: (1) annotation (an umbrella term for qualitative coding, labeling and text classification), (2) summarization, and (3) information extraction. The workflow is explicitly human-centered. Researchers design, supervise, and validate each stage of the LLM process to ensure rigor and transparency. Our approach synthesizes insights from extensive methodological literature across multiple disciplines: political science, sociology, computer science, psychology, and management. We outline validation procedures and best practices to address key limitations of LLMs, such as their black-box nature, prompt sensitivity, and tendency to hallucinate. To facilitate practical implementation, we provide supplementary materials, including a prompt library and Python code in Jupyter Notebook format, accompanied by detailed usage instructions.
Executive Summary
This article presents a human-centered workflow for using Large Language Models (LLMs) in content analysis, emphasizing the importance of leveraging LLMs through application programming interfaces (APIs) rather than chat-based access. The proposed workflow consists of three stages: annotation, summarization, and information extraction, with a focus on rigor, transparency, and validation. The authors draw on insights from various disciplines to address limitations of LLMs, including their black-box nature, prompt sensitivity, and tendency to hallucinate. The study provides supplementary materials, including a prompt library and Python code, to facilitate practical implementation. This workflow has the potential to streamline content analysis tasks and enhance the reliability of results.
Key Points
- ▸ LLMs can be leveraged through APIs for more effective content analysis
- ▸ The proposed workflow is human-centered, emphasizing rigor, transparency, and validation
- ▸ The study addresses limitations of LLMs, including their black-box nature and tendency to hallucinate
Merits
Comprehensive Approach
The study draws on a wide range of disciplines to develop a comprehensive workflow for content analysis using LLMs.
Practical Implementation
The study provides supplementary materials, including a prompt library and Python code, to facilitate practical implementation of the proposed workflow.
Addressing Limitations
The study addresses key limitations of LLMs, including their black-box nature and tendency to hallucinate.
Demerits
Dependence on LLMs
The proposed workflow relies on the availability and reliability of LLMs, which may be subject to limitations and biases.
Limited Generalizability
The study focuses on three specific content analysis tasks, which may not be generalizable to other tasks or domains.
Technical Expertise Required
The proposed workflow requires technical expertise in programming languages, such as Python, and may be inaccessible to non-technical researchers.
Expert Commentary
The study presents a timely and comprehensive approach to using LLMs in content analysis, addressing key limitations and providing practical guidance for implementation. However, the proposed workflow is not without its limitations, and further research is needed to address concerns related to dependence on LLMs and limited generalizability. Additionally, the study's reliance on technical expertise may limit accessibility to non-technical researchers. Nevertheless, the study's findings have significant implications for the field of automated content analysis and human-centered computing, and its practical implementation has the potential to enhance the reliability and efficiency of content analysis tasks.
Recommendations
- ✓ Further research is needed to address concerns related to dependence on LLMs and limited generalizability.
- ✓ The study's findings should be replicated and extended to other content analysis tasks and domains.
- ✓ The study's emphasis on human-centered design and validation procedures should be integrated into existing methods and tools for content analysis.
Sources
Original: arXiv - cs.AI
