Discourse integration in positional online news reader comments: Patterns of responsiveness across types of democracy, digital platforms, and perspective camps
Bibliographic record
Abstract
Online discourse integration, or the degree to which online user comments are responsive, that is, address or refer to other debate participants, is a normatively valued yet neglected quality dimension of online discussions. This preregistered study features the first cross-country/cross-platform investigation of online discourse integration, using manual and computational content analysis (N = 9835 and N = 30,753 positional news reader comments). Unexpectedly, about one quarter of the comments was responsive in both majoritarian and consensus-oriented democracies (Australia/United States vs Germany/Switzerland) and on platforms that separate or mix public and private contexts (websites vs Facebook pages of mainstream media), even though other deliberative quality criteria were previously shown to vary by country and platform. Comments that are responsive to fellow commenters in the opposing perspective camp were more likely to contain negative evaluations of those addressed, whereas comments responsive within the same perspective camp were more likely to contain positive evaluations.
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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 | 0.016 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".