The SFU Opinion and Comments Corpus: A Corpus for the Analysis of Online News Comments
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
in the 5-year period between 2012 and 2016, a total of 10,339 articles and 663,173 comments. SOCC is part of a project that investigates the linguistic characteristics of online comments. The corpus can be used to study a host of pragmatic phenomena. Among other aspects, researchers can explore: the connections between articles and comments; the connections of comments to each other; the types of topics discussed in comments; the nice (constructive) or mean (toxic) ways in which commenters respond to each other; how language is used to convey very specific types of evaluation; and how negation affects the interpretation of evaluative meaning in discourse. Our current focus is the study of constructiveness and evaluation in the comments. To that end, we have annotated a subset of the large corpus (1043 comments) with four layers of annotations: constructiveness, toxicity, negation and Appraisal (Martin and White, The language of evaluation, Palgrave, New York, 2005). This paper details our corpus, the data collection process, the characteristics of the corpus and describes the annotations. While our focus is comments posted in response to opinion news articles, the phenomena in this corpus are likely to be present in many commenting platforms: other news comments, comments and replies in fora such as Reddit, feedback on blogs, or YouTube comments.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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