Behavior Change Interventions Combating Online Misinformation: A Scoping Review

Online Harassment & Counter-ToolsMisinformation & Fact-CheckingParticipatory DesignFact-CheckersPrivacy Policy MakersContent Governance & Platform Compliance Teams

Research Background and Issues

  • What problems or challenges did the authors identify?
    The study highlights the widespread dissemination of misinformation in online environments, with two-thirds of online news consumers encountering false information at least once daily. Such misinformation is often spread through social media or digital platforms, and simply providing corrective information is insufficient to alter users' ingrained beliefs or attitudes; it may even have counterproductive effects.

  • Why is this issue important?
    The spread of misinformation not only affects individual decision-making but also leads to societal issues, such as the exacerbation of extreme beliefs and misunderstandings of public health policies. For example, vaccine hesitancy or refusal is partly a consequence of misinformation dissemination. Addressing this issue is critical for protecting societal trust and maintaining the authenticity of information.

  • Research Motivation and Related Work
    The motivation for this study lies in systematically reviewing existing behavioral intervention measures to understand their theoretical foundations, design and evaluation methods, and reasons for failure. Beyond technical misinformation detection, increasing research focuses on human behavioral interventions, leveraging psychology and behavioral theories to mitigate misinformation spread.

Solutions

  • What methods or solutions did the authors propose?
    The authors conducted an extensive literature review, analyzing 67 digital behavioral interventions to systematically summarize their behavioral goals, theoretical models, design methods, and reasons for failure. They also developed a set of design cards called “Behavioral Responses to Misinformation (BRM) Cards)” to support intervention designers.

  • What are the innovative aspects of the solution?
    The innovation lies in the classification framework. The study categorizes intervention goals into three stages of the "online news production process": information "creation," information "dissemination," and information "consumption," proposing 17 behavioral goals tailored to these stages. Additionally, 24 theoretical frameworks are listed to enhance the theoretical foundation of intervention designs, ranging from behavioral science to user-centered design theories.

  • What are the implementation steps and key technologies used?

    1. Behavioral Goal Classification: Goals are divided into the creation stage (e.g., reducing the generation of false information), dissemination stage (e.g., minimizing misinformation spread), and consumption stage (e.g., improving users' media literacy).
    2. Theoretical Frameworks: A sequence of 24 interdisciplinary theoretical models is outlined, including dual-process theories, motivational theories, and social cognitive theories.
    3. Design Methods: Methods include user-centered design, participatory design, and behavioral science frameworks, emphasizing the adoption of parallel design approaches.
    4. Evaluation and Experimentation: Intervention effectiveness is assessed through controlled laboratory experiments and real-world testing, with a focus on studying the long-term impact of interventions on behavior.

Research Outcomes

  • What specific outcomes were achieved?
    The study systematically summarized key design principles and influencing factors for behavioral interventions. It developed a set of design cards to assist researchers in defining goals, selecting theoretical frameworks, and preventing potential failure factors during intervention design. Additionally, the study identified multiple future directions, including expanding interventions targeting the early stages of news production and strengthening long-term evaluations of intervention effectiveness.

  • What advantages does it have compared to existing solutions?
    Compared to purely technical misinformation detection, this study emphasizes influencing user decisions and attitudes through behavioral interventions. The research methodology is systematic and comprehensive, highlighting the integration of theoretical foundations with practical applications. Furthermore, the design cards provide designers with direct and user-friendly tools.

  • What were the experimental or evaluation results?
    The study found that 69% of interventions underwent effectiveness evaluations, with 39% conducted in field tests rather than laboratory conditions, enhancing ecological validity. Additionally, 11% of interventions assessed their post-removal effects to better understand long-term impacts. Successful interventions accounted for 54%, while partially successful or failed interventions made up 46%.

  • Limitations and Future Directions
    Limitations include limited exploration of the early stages of news production (e.g., the creation stage) and a lack of extensive research on the effects of intervention removal. Future research could focus more on sustaining behavioral influence, optimizing factors contributing to failed interventions such as cultural adaptability, long-term behavior formation, and avoiding counterproductive intervention effects.


Summary: This study conducts a comprehensive literature review to explore the application of digital behavioral interventions in reducing misinformation, proposing a systematic analytical framework and developing practical tools. It emphasizes the integration of theory and practice while suggesting future research directions to further enhance the effectiveness and sustainability of interventions.

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https://hci.top/en/papers/chi/188996/2025

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DOI: https://dl.acm.org/doi/10.1145/3706598.3713127
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Source
CHI
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Year
2025
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2 authors
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Subtopics
Online Harassment & Counter-Tools, Misinformation & Fact-Checking, Participatory Design
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Fact-Checkers, Privacy Policy Makers, Content Governance & Platform Compliance Teams
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