Dataset Information

Opinion dynamics with backfire effect and biased assimilation.

ABSTRACT: The democratization of AI tools for content generation, combined with unrestricted access to mass media for all (e.g. through microblogging and social media), makes it increasingly hard for people to distinguish fact from fiction. This raises the question of how individual opinions evolve in such a networked environment without grounding in a known reality. The dominant approach to studying this problem uses simple models from the social sciences on how individuals change their opinions when exposed to their social neighborhood, and applies them on large social networks. We propose a novel model that incorporates two known social phenomena: (i) Biased Assimilation: the tendency of individuals to adopt other opinions if they are similar to their own; (ii) Backfire Effect: the fact that an opposite opinion may further entrench people in their stances, making their opinions more extreme instead of moderating them. To the best of our knowledge, this is the first DeGroot-type opinion formation model that captures the Backfire Effect. A thorough theoretical and empirical analysis of the proposed model reveals intuitive conditions for polarization and consensus to exist, as well as the properties of the resulting opinions.

SUBMITTER: Chen X

PROVIDER: S-EPMC8409649 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Opinion dynamics with backfire effect and biased assimilation.

Chen Xi X Tsaparas Panayiotis P Lijffijt Jefrey J De Bie Tijl T

PloS one 20210901 9

The democratization of AI tools for content generation, combined with unrestricted access to mass media for all (e.g. through microblogging and social media), makes it increasingly hard for people to distinguish fact from fiction. This raises the question of how individual opinions evolve in such a networked environment without grounding in a known reality. The dominant approach to studying this problem uses simple models from the social sciences on how individuals change their opinions when exp ...[more]

PMID: 34469486

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Project description:BackgroundDescriptive norms messaging interventions are used to motivate people to adopt or maintain desirable behaviors. Such interventions provide people with information that describes an undesirable behavior as uncommon or a desirable behavior as prevalent within a relevant social group. Descriptive norms messaging interventions have shown promise in increasing individual and social benefit for a broad range of health and sustainability programs. However, evidence suggests that people who have adopted desirable behaviors sometimes regress to undesirable behaviors after receiving descriptive norms messages due to the type of information provided in the messages. This phenomenon is called the boomerang effect. We aim to conduct a systematic review of boomerang effects on health and environmental sustainability behaviors resulting from exposure to descriptive norms messaging interventions.MethodsWe will employ our search strategy to identify studies of descriptive norms messaging interventions published prior to December 31, 2020. We will search the Cochrane Library, Campbell Library, PsycINFO, PubMed, Social Science Research Network (SSRN), and Web of Science to retrieve peer-reviewed articles published in English. We will restrict inclusion to studies (e.g., randomized and non-randomized controlled trials, quasi-experimental studies, and observational studies) of health and environmental sustainability interventions that assess behaviors before and after exposure to descriptive norms messaging. Two reviewers will independently extract data about study populations and design, intervention components, and behavioral measures. We will use the revised Cochrane Risk of Bias assessment tool (RoB2) and Risk Of Bias in Non-randomized Studies-of Intervention (ROBINS-I) to assess the risk of bias, and the Liverpool Quality Assessment Tool (LQAT) to assess the quality of evidence. We will conduct thematic analyses to codify interventions, and examine intervention effects across subgroups of individuals based on their behavior prior to intervention exposure (e.g., those practicing desirable behaviors vs. undesirable behaviors). We will also conduct moderator analyses to determine whether boomerang effects are contingent upon other factors including intervention framing and delivery modality.DiscussionThis systematic review will provide information about descriptive norms messaging intervention effects across subgroups of individuals and elucidate factors that potentially moderate boomerang effects. The review will yield evidence-based recommendations for the structure and content of descriptive norms messages that can be employed to avoid unintended boomerang effects within the context of health and sustainability programming.Systematic review registrationPROSPERO CRD42020156989.

Dataset Information

Opinion dynamics with backfire effect and biased assimilation.

Publications

Opinion dynamics with backfire effect and biased assimilation.

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