Proposing a practical taxonomy of misinformation for intervention design
Bibliographic record
Abstract
There has been a recent rise in the dissemination of misinformation worldwide, facilitated by social media and the use of technology, both of which amplify its spread. To inform interventions that can help tackle this issue, researchers have developed various taxonomies to summarize and categorize terms associated with misinformation. These taxonomies tend to focus on a subsection of terms associated with misinformation, such as fake news. In contrast, the present research provides a novel taxonomy that includes a more exhaustive list of types of misinformation based on existing taxonomies and relevant literature. A total of 51 terms were categorized on dimensions that fell under three categories: Psychological (intentionality, profit, ideological), Content (format, manipulation, facticity), and Source (audience, verifiability, agent). This taxonomy provides 9 dimensions that researchers and policymakers can use to better understand the characteristics of terms related to misinformation. In addition, terms can be clustered together so that interventions can be created to tackle like-terms.
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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 | 0.056 | 0.086 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".