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Young people and false information: A scoping review of responses, influential factors, consequences, and prevention programs

Kops, Maxime, Schittenhelm, Catherine, Wachs, Sebastian·2025·Computers in Human Behavior

Abstract

Today's digital landscape, marked by extensive use of information and communication technology, exposes young people to false information that shapes their perceptions of themselves and society. Yet, a comprehensive synthesis of research on false information among young people is lacking. This scoping review addressed this gap by exploring how young people respond to false information, the factors influencing their responses, the consequences of false information exposure, and existing prevention and intervention efforts. Out of 2255 identified records, 151 qualitative, quantitative, and mixed-method studies on young people aged 5 to 25 published before July 2024 were included. An adapted version of the Misinformation Recognition and Response Model was used to structure the findings. The analyses revealed that although young people often expressed high confidence in their judgments, they struggled to detect false information and respond passively – often ignoring rather than actively countering false information. Socio-demographic characteristics, cognitive biases, and social dynamics significantly shaped their responses. The consequences of these vulnerabilities were multifaceted, ranging from negative affective outcomes, such as confusion and fear, to broader societal impacts , including public disengagement and reduced political participation . To mitigate these risks, future strategies should focus on enhancing young people's ability to detect false information, empowerin…

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Metadata

Authors
Kops, M., Schittenhelm, C., & Wachs, S.
Venue
Computers in Human Behavior
Publisher
Elsevier BV
Published
2025-08
Volume
169
Pages
108650
ISSN
0747-5632
Cited by
39
References
20
Language
en

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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