A corpus analysis of online news comments using the Appraisal framework
Bibliographic record
Abstract
We present detailed analyses of the distribution of Appraisal categories (Martin and White, 2005) in a corpus of online news comments. The corpus consists of just over one thousand comments posted in response to a variety of opinion pieces on the website of the Canadian newspaper The Globe and Mail. We annotated all the comments with labels corresponding to different categories of the Appraisal framework. Analyses of the annotations show that comments are overwhelmingly negative, and that they favour two of the subtypes of Attitude (Judgment and Appreciation) over the third, Affect. The paper contributes a methodology for annotating Appraisal, and results that show the interaction of Appraisal with negation, the constructive (or not) nature of comments, and the level of toxicity found in them. The results show that highly opinionated language is expressed as an objective opinion (Judgement and Appreciation) rather than an emotional reaction (Affect). This finding, together with the interplay of evaluative language with constructiveness and toxicity in the comments, can be applied to the automatic moderation of comments.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".