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Paper Type
Complete
Description
Although the propagation of general information has received much attention, the investigation of misinformation diffusion lacks a citizen-to-authority perspective in the context of a crisis. Using the COVID-19 pandemic as an example, we develop and examine a research model hypothesising the underlying content-related factors contributing to the diffusion of misinformation. The study is tested by a dataset comprising 144K Twitter posts extracted from the United Kingdom from February 2020 to January 2022. We found that the frequency of three content topics (i.e., face protection, international economic support, and screening methods) is negatively associated with the diffusion of misinformation, whereas content similarity is not. Our analysis illustrates different influences of various trending topics on misinformation propagation in the setting of uncertainty management. Accordingly, policymakers, especially in times of crisis, should emphasize more information on preventive behaviours and provide factual messages that can improve public trust, reducing the possibility of misinformation spreading.
Paper Number
1373
Recommended Citation
Lu, Meichen; Ali, Maged; Kumar, Niraj; and Zhang, Wen, "Identification of the impact of content-related factors on the diffusion of misinformation: A Case study of the government intervention polices during COVID-19 pandemic in the UK" (2023). AMCIS 2023 Proceedings. 4.
https://aisel.aisnet.org/amcis2023/sig_dsa/sig_dsa/4
Identification of the impact of content-related factors on the diffusion of misinformation: A Case study of the government intervention polices during COVID-19 pandemic in the UK
Although the propagation of general information has received much attention, the investigation of misinformation diffusion lacks a citizen-to-authority perspective in the context of a crisis. Using the COVID-19 pandemic as an example, we develop and examine a research model hypothesising the underlying content-related factors contributing to the diffusion of misinformation. The study is tested by a dataset comprising 144K Twitter posts extracted from the United Kingdom from February 2020 to January 2022. We found that the frequency of three content topics (i.e., face protection, international economic support, and screening methods) is negatively associated with the diffusion of misinformation, whereas content similarity is not. Our analysis illustrates different influences of various trending topics on misinformation propagation in the setting of uncertainty management. Accordingly, policymakers, especially in times of crisis, should emphasize more information on preventive behaviours and provide factual messages that can improve public trust, reducing the possibility of misinformation spreading.
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