Misinformation

“Misinformation” refers to false or misleading information shared without intent to deceive. It differs from “disinformation,” which is intentionally deceptive. The spread of misinformation has intensified with the advent of digital technologies, particularly online social media and artificial intelligence (AI), enabling the rapid and large-scale dissemination of information. Belief in misinformation is influenced by cognitive biases, social identity, and emotional responses, while sharing behaviour may occur even without belief. Efforts to address misinformation include detection (e.g., fact-checking) and prevention (e.g., prebunking), often combining human and automated systems. Regulatory frameworks in regions such as the EU and the UK aim to increase platform accountability. Terminological inconsistencies, insufficient consideration of human aspects, and the dual role of AI tools, such as large language models (LLMs), present ongoing challenges.

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Publication Details

Journal
Kent Academic Repository (University of Kent)
Published
2026-04-01
Primary Topic
Misinformation and Its Impacts
Type
article
Field-Weighted Citation Impact
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article

Misinformation

Jason R. C. Nurse, Enes Altuncu, Shujun Li
Kent Academic Repository (University of Kent)
Misinformation and Its Impacts
article

Misinformation

Jason R. C. Nurse, Enes Altuncu, Shujun Li
article en

Abstract

“Misinformation” refers to false or misleading information shared without intent to deceive. It differs from “disinformation,” which is intentionally deceptive. The spread of misinformation has intensified with the advent of digital technologies, particularly online social media and artificial intelligence (AI), enabling the rapid and large-scale dissemination of information. Belief in misinformation is influenced by cognitive biases, social identity, and emotional responses, while sharing behaviour may occur even without belief. Efforts to address misinformation include detection (e.g., fact-checking) and prevention (e.g., prebunking), often combining human and automated systems. Regulatory frameworks in regions such as the EU and the UK aim to increase platform accountability. Terminological inconsistencies, insufficient consideration of human aspects, and the dual role of AI tools, such as large language models (LLMs), present ongoing challenges.

Kent Academic Repository (University of Kent)
Reduced inequalities
Openalex Percentile: Top 2%
Misinformation and Its Impacts
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