Engagement and constructiveness in online news comments in English and Russian
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 newspaper The Globe and Mail and the Russian online news channel RT . 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it