# Processes Matter: How ML/GAI Approaches Could Support Open Qualitative Coding of Online Discourse Datasets

arXiv: https://arxiv.org/abs/2504.02887 · HTML: https://mlsanity.com/paper/2504.02887/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 2 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [CoAIcoder: Examining the Effectiveness of AI-assisted Human-to-Human Collaboration in Qualitative Analysis](https://mlsanity.com/paper/2304.05560/) (2023)
- [Automating Thematic Analysis: How LLMs Analyse Controversial Topics](https://mlsanity.com/paper/2405.06919/) (2024)
- [A Computational Method for Measuring "Open Codes" in Qualitative Analysis](https://mlsanity.com/paper/2411.12142/) (2024)
- [Prompts Matter: Comparing ML/GAI Approaches for Generating Inductive Qualitative Coding Results](https://mlsanity.com/paper/2411.06316/) (2024)

## Built on by (most cited first, 1 of 1)

- [What Do People Actually Want From AI? Mapping Preference Plurality](https://mlsanity.com/paper/2606.06674/) (2026)

## Abstract (arXiv)

Open coding, a key inductive step in qualitative research, discovers and constructs concepts from human datasets. However, capturing extensive and nuanced aspects or "coding moments" can be challenging, especially with large discourse datasets. While some studies explore machine learning (ML)/Generative AI (GAI)'s potential for open coding, few evaluation studies exist. We compare open coding results by five recently published ML/GAI approaches and four human coders, using a dataset of online chat messages around a mobile learning software. Our systematic analysis reveals ML/GAI approaches' strengths and weaknesses, uncovering the complementary potential between humans and AI. Line-by-line AI approaches effectively identify content-based codes, while humans excel in interpreting conversational dynamics. We discussed how embedded analytical processes could shape the results of ML/GAI approaches. Instead of replacing humans in open coding, researchers should integrate AI with and according to their analytical processes, e.g., as parallel co-coders.