ArticleEPJ data science2024
Novel embeddings improve the prediction of risk perception.
Article in EPJ data science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Assessment tools for disease risk perception in chronic patients: theoretical frameworks, psychometric properties, and clinical applications.Frontiers in public health · 2026Review
- Measuring individual semantic networks: A simulation study.PloS one · 2025Article
- Mapping Mental Representations With Free Associations: A Tutorial Using the R Package associatoR.Journal of cognition · 2025Article
- A tutorial on open-source large language models for behavioral science.Behavior research methods · 2024Article
- Individual and team performance in cricket.Royal Society open science · 2024Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We assess whether the classic psychometric paradigm of risk perception can be improved or supplanted by novel approaches relying on language embeddings. To this end, we introduce the Basel Risk Norms, a large data set covering 1004 distinct sources of risk (e.g., vaccination, nuclear energy, artificial intelligence) and compare the psychometric paradigm against novel text and free-association embeddings in predicting risk perception. We find that an ensemble model combining text and free association rivals the predictive accuracy of the psychometric paradigm, captures additional affect and frequency-related dimensions of risk perception not accounted for by the classic approach, and has greater range of applicability to real-world text data, such as news headlines. Overall, our results establish the ensemble of text and free-association embeddings as a promising new tool for researchers and policymakers to track real-world risk perception. Supplementary Information: The online version contains supplementary material available at 10.1140/epjds/s13688-024-00478-x.
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