Linguistic approaches to fake news research are growing and maturing: commentary on a special issue
Bibliographic record
Abstract
Abstract This first special issue of Linguistics Vanguard on the language of fake news offers multiple perspectives on the linguistic analysis of fake news and misinformation, with a range of approaches and methodologies, showing the sophistication and maturity of the field. The study of fake news is a flourishing area of research, with contributions from communication, media studies, data science, and natural language processing. The growing body of research in linguistics contributes careful qualitative analyses of the social context of production and interpretation of misinformation, coupled with well-documented techniques from corpus linguistics. This commentary provides a short retrospective of linguistic approaches to fake news and highlights the contributions from the papers in the issue.
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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 | 0.033 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.016 | 0.028 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.060 | 0.068 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".