Engagement and constructiveness in online news comments in English and Russian
Bibliographic record
Abstract
Abstract We investigate the relationship between Engagement and constructiveness in online news comments by analyzing the frequency and type of Engagement expressions in a corpus of English and Russian comments, following the Appraisal framework. The comments in question, 10,000 words in each language, were posted in response to opinion articles in the Canadian newspaperThe Globe and Mailand the Russian online news channelRT. In the context of online news comments, users generally characterize constructive comments as posts that tend to create a civil dialogue through remarks that are relevant to the article and do not provoke an emotional response. Through quantitative and qualitative analyses, we conclude that the language of constructive comments is more explicitly subjective in both languages. The main difference in the use of Engagement expressions in constructive and non-constructive comments lies along the lines of certainty/uncertainty and reliability/unreliability. As for cross-linguistic differences, it seems that English constructive comments place emphasis on the reliability of a commenter’s knowledge, while Russian constructive comments employ more modals of necessity, which have a prescriptive function.
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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 | 0.006 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".