Nudge Users to Healthier Decisions: A Design Approach to Encounter Misinformation in Health Forums

Ebnali, Mahdi; Kian, Cyrus

doi:10.1007/978-3-030-20500-3_1

Cited by 8 publications

(1 citation statement)

References 22 publications

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“…According to the elaboration likelihood model (ELM), a widely used persuasion model, users form their attitudes toward a message using either the central or peripheral path [ 5 ]. In the central path, users evaluate the quality and strength of the information, whereas in the peripheral route, they focus more on superficial factors such as the source’s reputation, visual appeal, and presentation [ 6 ]. Therefore, the characteristics of online health misinformation can be divided into two levels: central and peripheral [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Understanding How and by Whom COVID-19 Misinformation is Spread on Social Media: Coding and Network Analyses

Zhao¹,

Zhu²,

Wan³

et al. 2022

J Med Internet Res

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Background During global health crises such as the COVID-19 pandemic, rapid spread of misinformation on social media has occurred. The misinformation associated with COVID-19 has been analyzed, but little attention has been paid to developing a comprehensive analytical framework to study its spread on social media. Objective We propose an elaboration likelihood model–based theoretical model to understand the persuasion process of COVID-19–related misinformation on social media. Methods The proposed model incorporates the central route feature (content feature) and peripheral features (including creator authority, social proof, and emotion). The central-level COVID-19–related misinformation feature includes five topics: medical information, social issues and people’s livelihoods, government response, epidemic spread, and international issues. First, we created a data set of COVID-19 pandemic–related misinformation based on fact-checking sources and a data set of posts that contained this misinformation on real-world social media. Based on the collected posts, we analyzed the dissemination patterns. Results Our data set included 11,450 misinformation posts, with medical misinformation as the largest category (n=5359, 46.80%). Moreover, the results suggest that both the least (4660/11,301, 41.24%) and most (2320/11,301, 20.53%) active users are prone to sharing misinformation. Further, posts related to international topics that have the greatest chance of producing a profound and lasting impact on social media exhibited the highest distribution depth (maximum depth=14) and width (maximum width=2355). Additionally, 97.00% (2364/2437) of the spread was characterized by radiation dissemination. Conclusions Our proposed model and findings could help to combat the spread of misinformation by detecting suspicious users and identifying propagation characteristics.

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Section: Introductionmentioning

confidence: 99%