From Transformers Come Themes: Evaluating BERTopic for Qualitative Analysis of Social Media Data
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.102 | 0.278 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".