Can we use automated approaches to measure the quality of online political discussion? How to (not) measure interactivity, diversity, rationality, and incivility in online comments to the news
Bibliographic record
Abstract
This article explores the (in)ability of automated tools to measure the deliberative quality of online user comments along the standards set out by Habermas: interactivity, diversity, rationality, and (in)civility. Utilizing a stratified sample of manually coded comments (n = 3,862) responding to news videos on YouTube and Twitter, we examined the performance of rule-based measures (i.e. dictionaries), machine-learning classifiers (conventional and transformer-based) and measurements by generative AI (Llama 3.1, GPT-4o, GPT-4T). We present results for over 50 metrics side-by-side to judge the opportunity costs of choosing one method over another. The results revealed strong variation across different groups of models. Overall, our expectation that more modern methods (transformers and generative AI) outperform the older, simpler ones was confirmed. However, the absolute differences between these model groups strongly depended on the measured concept, and we observed strong variance in performance among models of the same group. We provide recommendations for future research that balance ease of use with the performance of automated measurements, along with important cautions to consider.
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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 | 0.048 | 0.274 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".