Unravelling misinformation in politics: introduction to the special issue
Bibliographic record
Abstract
Misinformation is discussed as one of the most pressing challenges of the digital age, potentially shaping public opinion and influencing political behaviour across the globe. In this introduction to a special issue, we report on the analysis of Scopus search results documenting increasing attention to misinformation (and related topics) over the past 25 years and a focus on the US context. The contributions to this special issue consider the US context (Gomez and Jenkins 2025; Littrell et al. 2025), but also consider Brazil (Bastos et al. 2025), Germany (Unger et al. 2025), and several cross-national studies that include other countries in Europe, the US, and Canada (Holt and Bechmann 2025; Hoffmann and Boulianne 2025; Morosoli and Humprecht 2025). The contributions offer overlapping insights on the themes of misinformation as a socially constructed phenomenon; misinformation exposure and engagement; and the power and limitations of misinformation. We connect these new findings with recently published meta-analyses and systematic literature reviews on misinformation. We conclude with suggestions for future research that addresses contextual and cultural specificity; examines the motivations for engagement; explores new angles for the study of polarization and partisanship; and new methodologies.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".