tetano
Editor, Senior Moderator
J Med Internet Res
. 2022 Jun 7.
doi: 10.2196/37623. Online ahead of print.
Understanding How and by Whom COVID-19 Misinformation is Spread on Social Media: Coding and Network Analyses
Yuehua Zhao[SUP] 1 2 [/SUP], Sicheng Zhu[SUP] 3 [/SUP], Qiang Wan[SUP] 3 [/SUP], Tianyi Li[SUP] 3 [/SUP], Chun Zou[SUP] 3 [/SUP], Hao Wang[SUP] 1 2 [/SUP], Sanhong Deng[SUP] 1 2 [/SUP]
Affiliations
Abstract
Background: During global health crises, such as the coronavirus disease (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 incorporated the central route feature (content feature) and peripheral features (including creator authority, social proof, and emotion). The central-level COVID-19-related misinformation feature included five topics: medical information, social issues and people's livelihoods, government response, epidemic spread, and international issues. First, we created a dataset of COVID-19 pandemic-related misinformation based on fact-checking sources and a dataset of posts that contained this misinformation on real-world social media. Based on the collected posts, we analyzed the dissemination patterns.
Results: Our dataset included 11,450 misinformation posts, with medical misinformation as the largest category (5,359; 46.8 %). Moreover, the results suggest that both the least (4,660/11,301; 41.2 %) and most (2,320/11301; 20.5 %) 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 possess the highest distribution depth (depthmax=14) and width (widthmax=2,355). Additionally, 97% (2,364/2,437) of the spread is 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.
. 2022 Jun 7.
doi: 10.2196/37623. Online ahead of print.
Understanding How and by Whom COVID-19 Misinformation is Spread on Social Media: Coding and Network Analyses
Yuehua Zhao[SUP] 1 2 [/SUP], Sicheng Zhu[SUP] 3 [/SUP], Qiang Wan[SUP] 3 [/SUP], Tianyi Li[SUP] 3 [/SUP], Chun Zou[SUP] 3 [/SUP], Hao Wang[SUP] 1 2 [/SUP], Sanhong Deng[SUP] 1 2 [/SUP]
Affiliations
- PMID: 35671411
- DOI: 10.2196/37623
Abstract
Background: During global health crises, such as the coronavirus disease (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 incorporated the central route feature (content feature) and peripheral features (including creator authority, social proof, and emotion). The central-level COVID-19-related misinformation feature included five topics: medical information, social issues and people's livelihoods, government response, epidemic spread, and international issues. First, we created a dataset of COVID-19 pandemic-related misinformation based on fact-checking sources and a dataset of posts that contained this misinformation on real-world social media. Based on the collected posts, we analyzed the dissemination patterns.
Results: Our dataset included 11,450 misinformation posts, with medical misinformation as the largest category (5,359; 46.8 %). Moreover, the results suggest that both the least (4,660/11,301; 41.2 %) and most (2,320/11301; 20.5 %) 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 possess the highest distribution depth (depthmax=14) and width (widthmax=2,355). Additionally, 97% (2,364/2,437) of the spread is 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.