Modeling the amplification of epidemic spread by individuals exposed to misinformation on social media
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arXiv
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| Format: | Preprint |
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2024
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| author | DeVerna, Matthew R. Pierri, Francesco Ahn, Yong-Yeol Fortunato, Santo Flammini, Alessandro Menczer, Filippo |
| author_facet | DeVerna, Matthew R. Pierri, Francesco Ahn, Yong-Yeol Fortunato, Santo Flammini, Alessandro Menczer, Filippo |
| contents | Understanding how misinformation affects the spread of disease is crucial for public health, especially given recent research indicating that misinformation can increase vaccine hesitancy and discourage vaccine uptake. However, it is difficult to investigate the interaction between misinformation and epidemic outcomes due to the dearth of data-informed holistic epidemic models. Here, we employ an epidemic model that incorporates a large, mobility-informed physical contact network as well as the distribution of misinformed individuals across counties derived from social media data. The model allows us to simulate various scenarios to understand how epidemic spreading can be affected by misinformation spreading through one particular social media platform. Using this model, we compare a worst-case scenario, in which individuals become misinformed after a single exposure to low-credibility content, to a best-case scenario where the population is highly resilient to misinformation. We estimate the additional portion of the U.S. population that would become infected over the course of the COVID-19 epidemic in the worst-case scenario. This work can provide policymakers with insights about the potential harms of exposure to online vaccine misinformation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_11351 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Modeling the amplification of epidemic spread by individuals exposed to misinformation on social media DeVerna, Matthew R. Pierri, Francesco Ahn, Yong-Yeol Fortunato, Santo Flammini, Alessandro Menczer, Filippo Social and Information Networks Computers and Society Physics and Society Understanding how misinformation affects the spread of disease is crucial for public health, especially given recent research indicating that misinformation can increase vaccine hesitancy and discourage vaccine uptake. However, it is difficult to investigate the interaction between misinformation and epidemic outcomes due to the dearth of data-informed holistic epidemic models. Here, we employ an epidemic model that incorporates a large, mobility-informed physical contact network as well as the distribution of misinformed individuals across counties derived from social media data. The model allows us to simulate various scenarios to understand how epidemic spreading can be affected by misinformation spreading through one particular social media platform. Using this model, we compare a worst-case scenario, in which individuals become misinformed after a single exposure to low-credibility content, to a best-case scenario where the population is highly resilient to misinformation. We estimate the additional portion of the U.S. population that would become infected over the course of the COVID-19 epidemic in the worst-case scenario. This work can provide policymakers with insights about the potential harms of exposure to online vaccine misinformation. |
| title | Modeling the amplification of epidemic spread by individuals exposed to misinformation on social media |
| topic | Social and Information Networks Computers and Society Physics and Society |
| url | https://arxiv.org/abs/2402.11351 |