The global spread of misinformation on spiders
Bibliographic record
Abstract
In the internet era, the digital architecture that keeps us connected and informed may also amplify the spread of misinformation. This problem is gaining global attention, as evidence accumulates that misinformation may interfere with democratic processes and undermine collective responses to environmental and health crises1West J.D. Bergstrom C.T. Misinformation in and about science.Proc. Natl. Acad. Sci. USA. 2021; 118e1912444117Crossref Scopus (24) Google Scholar,2Zarocostas J. How to fight an infodemic.Lancet. 2020; 395: 676Abstract Full Text Full Text PDF PubMed Scopus (785) Google Scholar. In an increasingly polluted information ecosystem, understanding the factors underlying the generation and spread of misinformation is becoming a pressing scientific and societal challenge3Acerbi A. Cognitive attraction and online misinformation.Palgrave Commun. 2019; 5: 15Crossref Scopus (35) Google Scholar. Here, we studied the global spread of (mis-)information on spiders using a high-resolution global database of online newspaper articles on spider–human interactions, covering stories of spider–human encounters and biting events published from 2010–20204Mammola S. Malumbres-Olarte J. Arabesky V. Barrales-Alcalá D.A. Barrion-Dupo A.L. Benamú M.A. Bird T.L. Bogomolova M. Cardoso P. Chatzaki M. et al.An expert-curated global database of online newspaper articles on spiders and spider bites.Sci. Data. 2022; 9: 109Crossref Scopus (1) Google Scholar. We found that 47% of articles contained errors and 43% were sensationalist. Moreover, we show that the flow of spider-related news occurs within a highly interconnected global network and provide evidence that sensationalism is a key factor underlying the spread of misinformation.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".