LATA: A Pilot Study on LLM-Assisted Thematic Analysis of Online Social Network Data Generation Experiences
Abstract
Large Language Models (LLMs) have gained attention in research and industry, aiming to streamline processes and enhance text analysis performance. Thematic Analysis (TA), a prevalent qualitative method for analyzing interview content, often requires at least two human experts to review and analyze data. This study demonstrates the feasibility of LLM-Assisted Thematic Analysis (LATA) using GPT-4 and Gemini. Specifically, we conducted semi-structured interviews with 14 researchers to gather insights on their experiences generating and analyzing Online Social Network (OSN) communications datasets. Following Braun and Clarke's six-phase TA framework with an inductive approach, we initially analyzed our interview transcripts with human experts. Subsequently, we iteratively designed prompts to guide LLMs through a similar process. We compare and discuss the manually analyzed outcomes with responses generated by LLMs and achieve a cosine similarity score up to 0.76, demonstrating a promising prospect for LATA. Additionally, the study delves into researchers' experiences navigating the complexities of collecting and analyzing OSN data, offering recommendations for future research and application designers.
Cite this
Qile Wang, Moath Erqsous, Kenneth E. Barner, Matthew Louis Mauriello (2025). LATA: A Pilot Study on LLM-Assisted Thematic Analysis of Online Social Network Data Generation Experiences. Proceedings of the ACM on Human-Computer Interaction. https://doi.org/10.1145/3711022
@article{qile2025lata,
title = {LATA: A Pilot Study on LLM-Assisted Thematic Analysis of Online Social Network Data Generation Experiences},
author = {Wang, Qile and Erqsous, Moath and Barner, Kenneth E. and Mauriello, Matthew Louis},
year = {2025},
journal = {Proceedings of the ACM on Human-Computer Interaction},
doi = {10.1145/3711022}
}Citations in this library
Stored only when both papers are in the corpus — 20 citations exist upstream.