From Transformers Come Themes: Evaluating BERTopic for Qualitative Analysis of Social Media Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Social media constitutes a rich and influential source of information for qualitative researchers, but its scale can prohibit in-depth study. In this paper we explore how BERTopic, a topic modelling technique that leverages transformer-based sentence embeddings, can support qualitative data analysis of social media. We conducted interviews and hands-on evaluations in which qualitative researchers compared topics generated by BERTopic to those from two established unsupervised modelling techniques: Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF). BERTopic was cited as the preferred technique by 8 of 12 participants for its ability to provide detailed, coherent clusters. Participants also emphasized the relevance of its topics, their logical organization, and the capacity to reveal unexpected relationships within the data. Our findings underscore the potential of sentence encoder-based topic modelling techniques for supporting qualitative analysis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it