@article{ref1,
title="Novel embeddings improve the prediction of risk perception",
journal="EPJ data science",
year="2024",
author="Hussain, Zak and Mata, Rui and Wulff, Dirk U.",
volume="13",
number="1",
pages="e38-e38",
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.<p /> <p>Language: en</p>",
language="en",
issn="2193-1127",
doi="10.1140/epjds/s13688-024-00478-x",
url="http://dx.doi.org/10.1140/epjds/s13688-024-00478-x"
}