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Abstract
Empirical social sciences routinely model misinformation as exhibiting dynamics analogous to vaccinable diseases or contagious outbreaks, as in inoculation theory and other epidemiological models. However, idiosyncratic features of the social construction of misinformation violate the biological analogy in significant ways, rendering these models far weaker in effect size, predictive accuracy, and explanatory power than has been claimed. Four arguments are discussed regarding problems with the ontology of misinformation posited in these models, methods for measuring misinformation, individuation of mechanisms, and application of interventions. A conclusion is drawn that model transfer from biology has often been unwarranted in misinformation studies and that alternative methods should be pursued instead.
| Original language | English |
|---|---|
| Article number | 158 |
| Number of pages | 23 |
| Journal | Synthese |
| Volume | 206 |
| Issue number | 3 |
| Early online date | 10 Sept 2025 |
| DOIs | |
| Publication status | Published - 10 Sept 2025 |
Bibliographical note
I thank the following for critical feedback on ideas in this paper: Kenji Hayakawa, Daniel Munro, and four anonymous referees. All errors and infelicities are mine alone.Publisher Copyright:
© The Author(s) 2025.
Funding
Open Access Publishing Support Fund provided by Lingnan University I thank the following for critical feedback on ideas in this paper: Kenji Hayakawa, Daniel Munro, and four anonymous referees. I acknowledge funding from the Hong Kong Catastrophic Risk Centre and two Hong Kong government research grants (#101914 and #1859249) identically titled ‘Machine Learning Models of Misinformation and Deceptive Media’. All errors and infelicities are mine alone.
Keywords
- Misinformation
- Social epistemology
- Model transfer
- Philosophy of science
- Philosophy of social science
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Machine Learning Models of Misinformation and Deceptive Media
YEE, A. K. (PI)
1/01/24 → 1/01/26
Project: Grant Research