Automated Thematic Analyses Using LLMs: Xylazine Wound Management Social Media Chatter Use Case

• URL: http://arxiv.org/abs/2507.10803v1
• Date: Mon, 14 Jul 2025 20:57:52 GMT
• Title: Automated Thematic Analyses Using LLMs: Xylazine Wound Management Social Media Chatter Use Case
• Authors: JaMor Hairston, Ritvik Ranjan, Sahithi Lakamana, Anthony Spadaro, Selen Bozkurt, Jeanmarie Perrone, Abeed Sarker,
• Abstract summary: Large language models (LLMs) face challenges in inductive thematic analysis.<n>We evaluated the feasibility of using LLMs to replicate expert-driven thematic analysis of social media data.
• Score: 2.583403860629219
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Background Large language models (LLMs) face challenges in inductive thematic analysis, a task requiring deep interpretive and domain-specific expertise. We evaluated the feasibility of using LLMs to replicate expert-driven thematic analysis of social media data. Methods Using two temporally non-intersecting Reddit datasets on xylazine (n=286 and n=686, for model optimization and validation, respectively) with twelve expert-derived themes, we evaluated five LLMs against expert coding. We modeled the task as a series of binary classifications, rather than a single, multi-label classification, employing zero-, single-, and few-shot prompting strategies and measuring performance via accuracy, precision, recall, and F1-score. Results On the validation set, GPT-4o with two-shot prompting performed best (accuracy: 90.9%; F1-score: 0.71). For high-prevalence themes, model-derived thematic distributions closely mirrored expert classifications (e.g., xylazine use: 13.6% vs. 17.8%; MOUD use: 16.5% vs. 17.8%). Conclusions Our findings suggest that few-shot LLM-based approaches can automate thematic analyses, offering a scalable supplement for qualitative research. Keywords: thematic analysis, large language models, natural language processing, qualitative analysis, social media, prompt engineering, public health

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