The anatomy of ‘fake news’: Studying false messages as digital objects
Bibliographic record
Abstract
Public concern about ‘fake news’ skyrocketed following the 2016 US presidential election and the Brexit referendum, and has only intensified since then. A burgeoning body of research on the topic is emerging, and conceptual clarity is vital for this research to converge into a cumulative body of knowledge; the purpose of this article is to underline and address some of the conceptual clutter and ambiguities around the concept of fake news and situate it within its social context. To do so, we first discuss the problems with current terminology and conceptualisation, and then draw on recent developments on the ontology of digital objects and their attributes to shift the focus from fake news to false messages, a type of syntactic digital objects comprised of content and structure and characterised by attributes of editability, openness, interactivity, and distributedness. Then we expand this concept further by placing it within a network of actors and digital objects. Our analysis uncovers several areas of research that have been overlooked in the study of fake news.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".