The risk co-de model: detecting psychosocial processes of risk perception in natural language through machine learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A6ZEIXEB6" target="_blank" >RIV/00216208:11320/25:6ZEIXEB6 - isvavai.cz</a>

Alternative codes found

RIV/00216208:11320/23:NTXATQRM

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178230438&doi=10.1007%2fs42001-023-00235-6&partnerID=40&md5=f395bb7d69012f2bd293121e69a76371" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178230438&doi=10.1007%2fs42001-023-00235-6&partnerID=40&md5=f395bb7d69012f2bd293121e69a76371</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s42001-023-00235-6" target="_blank" >10.1007/s42001-023-00235-6</a>

Result language

angličtina

Original language name

The risk co-de model: detecting psychosocial processes of risk perception in natural language through machine learning

Original language description

This paper presents a classification system (risk Co-De model) based on a theoretical model that combines psychosocial processes of risk perception, including denial, moral disengagement, and psychological distance, with the aim of classifying social media posts automatically, using machine learning algorithms. The risk Co-De model proposes four macro-categories that include nine micro-categories defining the stance towards risk, ranging from Consciousness to Denial (Co-De). To assess its effectiveness, a total of 2381 Italian tweets related to risk events (such as the Covid-19 pandemic and climate change) were manually annotated by four experts according to the risk Co-De model, creating a training set. Each category was then explored to assess its peculiarity by detecting co-occurrences and observing prototypical tweets classified as a whole. Finally, machine learning algorithms for classification (Support Vector Machine and Random Forest) were trained starting from a text chunks x (multilevel) features matrix. The Support Vector Machine model trained on the four macro-categories achieved an overall accuracy of 86% and a macro-average F1 score of 0.85, indicating good performance. The application of the risk Co-De model addresses the challenge of automatically identifying psychosocial processes in natural language, contributing to the understanding of the human approach to risk and informing tailored communication strategies. © The Author(s) 2023. corrected publication 2023.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Computational Social Science

ISSN

2432-2717

e-ISSN

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Volume of the periodical

7

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

23

Pages from-to

217-239

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85178230438