UNDERSTANDING ‘FAKE NEWS’: A BIBLIOGRAPHIC PERSPECTIVE
Bibliographic record
Abstract
False information that appears similar to trustworthy media content, or what is commonly referred to as ‘fake news’, is pervasive in both traditional and digital strategic communication channels. This paper presents a comprehensive bibliographic analysis of published academic articles related to ‘fake news’ and the related concepts of truthiness, post-factuality, and deepfakes. Using the Web of Science database and VOSViewer software, papers published on these topics were extracted and analysed to identify and visualise key trends, influential authors, and journals focusing on these topics. Articles in our dataset tend to cite authors, papers, and journals that are also within the dataset, suggesting that the conversation surrounding ‘fake news’ is still relatively centralised. Based on our findings, this paper develops a conceptual ‘fake news’ framework—derived from variations of the intention to deceive and/or harm—classifying ‘fake news’ into four subtypes: mis-information, dis-information, mal-information, and non-information. We conclude that most existing studies of ‘fake news’ investigate mis-information and dis-information, thus we suggest further study of mal-information and non-information. This paper helps scholars, practitioners, and global policy makers who wish to understand the current state of the academic conversation related to ‘fake news’, and to determine important areas for further research.
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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 | 0.018 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.100 | 0.106 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.026 | 0.029 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".