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Incorporating Psychological Science Into Policy Making: The Case of Misinformation.

ABSTRACT: The spread of false and misleading information in online social networks is a global problem in need of urgent solutions. It is also a policy problem because misinformation can harm both the public and democracies. To address the spread of misinformation, policymakers require a successful interface between science and policy, as well as a range of evidence-based solutions that respect fundamental rights while efficiently mitigating the harms of misinformation online. In this article, we discuss how regulatory and nonregulatory instruments can be informed by scientific research and used to reach EU policy objectives. First, we consider what it means to approach misinformation as a policy problem. We then outline four building blocks for cooperation between scientists and policymakers who wish to address the problem of misinformation: understanding the misinformation problem, understanding the psychological drivers and public perceptions of misinformation, finding evidence-based solutions, and co-developing appropriate policy measures. Finally, through the lens of psychological science, we examine policy instruments that have been proposed in the EU, focusing on the strengthened Code of Practice on Disinformation 2022.

SUBMITTER: Kozyreva A

PROVIDER: S-EPMC7615323 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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Incorporating Psychological Science Into Policy Making: The Case of Misinformation.

Kozyreva Anastasia A Smillie Laura L Lewandowsky Stephan S

European psychologist 20230714 3

The spread of false and misleading information in online social networks is a global problem in need of urgent solutions. It is also a policy problem because misinformation can harm both the public and democracies. To address the spread of misinformation, policymakers require a successful interface between science and policy, as well as a range of evidence-based solutions that respect fundamental rights while efficiently mitigating the harms of misinformation online. In this article, we discuss ...[more]

PMID: 37994309

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Project description:The Lisbon Strategy was adopted by the Heads of State and Government of the European Union (EU) in 2000. By moving science into a central position for the development of a European knowledge-based economy and society, its adoption at political level seems to have been a powerful catalyst for the increased involvement of scientists in science policy in the EU. Recognising the need for scientists to act collectively in order to contribute to shape the future of science policy in Europe, a pioneering group of European science organisations leaders and representatives, as well as other scientists, initiated a European, interdisciplinary, inclusive movement leading to the creation of the European Research Council (ERC) to support basic research of the highest quality. Having scientists' campaign for the funding of bottom-up research by the EU Framework Programmes exclusively on scientific grounds, and for an ERC, was a unique event in the recent history of European science policy. For the first time, the scientific community acted collectively and across disciplinary or national boundaries as a political actor for the sake of a better science policy for Europe. As is often the case when first-hand experience is gained through the creation of a new organization, novel forms of collaboration arise. The European biomedical community has recently proposed the creation of a strategic action plan for health research (the European Council of Health Research; EuCHR), provisionally translated at present into a Scientific Panel for Health (SPH) research in Horizon 2020, the EU's research-funding programme for the period 2014-2020. The creation of such Scientific Panel should be viewed as an important contribution by the biomedical community as a major political agreement has been reached on the need for a comprehensive and long-term scientific strategy to accelerate research and facilitate innovation at EU level. It is our belief that describing and analyzing the process leading to the creation of the ERC and SPH (2002-2014) should be widely shared with the research community in general, as this may contribute to the understanding of the evolving relations between scientists and science-policy making.

Project description:IntroductionThe rise of social media users and the explosive growth in misinformation shared across social media platforms have become a serious threat to democratic discourse and public health. The mentioned implications have increased the demand for misinformation detection and intervention. To contribute to this challenge, we are presenting a systematic scoping review of psychological interventions countering misinformation in social media. The review was conducted to (i) identify and map evidence on psychological interventions countering misinformation, (ii) compare the viability of the interventions on social media, and (iii) provide guidelines for the development of effective interventions.MethodsA systematic search in three bibliographic databases (PubMed, Embase, and Scopus) and additional searches in Google Scholar and reference lists were conducted.Results3,561 records were identified, 75 of which met the eligibility criteria for the inclusion in the final review. The psychological interventions identified during the review can be classified into three categories distinguished by Kozyreva et al.: Boosting, Technocognition, and Nudging, and then into 15 types within these. Most of the studied interventions were not implemented and tested in a real social media environment but under strictly controlled settings or online crowdsourcing platforms. The presented feasibility assessment of implementation insights expressed qualitatively and with numerical scoring could guide the development of future interventions that can be successfully implemented on social media platforms.DiscussionThe review provides the basis for further research on psychological interventions counteracting misinformation. Future research on interventions should aim to combine effective Technocognition and Nudging in the user experience of online services.Systematic review registration[https://figshare.com/], identifier [https://doi.org/10.6084/m9.figshare.14649432.v2].

Project description:Background. Accurate diagnosis of patients' preferences is central to shared decision making. Missing from clinical practice is an approach that links pretreatment preferences and patient-reported outcomes. Objective. We propose a Bayesian collaborative filtering (CF) algorithm that combines pretreatment preferences and patient-reported outcomes to provide treatment recommendations. Design. We present the methodological details of a Bayesian CF algorithm designed to accomplish 3 tasks: 1) eliciting patient preferences using conjoint analysis surveys, 2) clustering patients into preference phenotypes, and 3) making treatment recommendations based on the posttreatment satisfaction of like-minded patients. We conduct a series of simulation studies to test the algorithm and to compare it to a 2-stage approach. Results. The Bayesian CF algorithm and 2-stage approaches performed similarly when there was extensive overlap between preference phenotypes. When the treatment was moderately associated with satisfaction, both methods made accurate recommendations. The kappa estimates measuring agreement between the true and predicted recommendations were 0.70 (95% confidence interval = 0.052-0.88) and 0.73 (0.56-0.90) under the Bayesian CF and 2-stage approaches, respectively. The 2-stage approach failed to converge in settings in which clusters were well separated, whereas the Bayesian CF algorithm produced acceptable results, with kappas of 0.73 (0.56-0.90) and 0.83 (0.69-0.97) for scenarios with moderate and large treatment effects, respectively. Limitations. Our approach assumes that the patient population is composed of distinct preference phenotypes, there is association between treatment and outcomes, and treatment effects vary across phenotypes. Findings are also limited to simulated data. Conclusion. The Bayesian CF algorithm is feasible, provides accurate cluster treatment recommendations, and outperforms 2-stage estimation when clusters are well separated. As such, the approach serves as a roadmap for incorporating predictive analytics into shared decision making.

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Incorporating Psychological Science Into Policy Making: The Case of Misinformation.

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Incorporating Psychological Science Into Policy Making: The Case of Misinformation.

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