Modeling the amplification of epidemic spread by individuals exposed to misinformation on social media
Abstract 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 misinfor...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | npj Complexity |
| Online Access: | https://doi.org/10.1038/s44260-025-00038-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849733370563526656 |
|---|---|
| author | Matthew R. DeVerna Francesco Pierri Yong-Yeol Ahn Santo Fortunato Alessandro Flammini Filippo Menczer |
| author_facet | Matthew R. DeVerna Francesco Pierri Yong-Yeol Ahn Santo Fortunato Alessandro Flammini Filippo Menczer |
| author_sort | Matthew R. DeVerna |
| collection | DOAJ |
| description | Abstract 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 | Article |
| id | doaj-art-9c2dda42b7004cb5bff7a827b5a0e07f |
| institution | DOAJ |
| issn | 2731-8753 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Complexity |
| spelling | doaj-art-9c2dda42b7004cb5bff7a827b5a0e07f2025-08-20T03:08:02ZengNature Portfolionpj Complexity2731-87532025-04-01211810.1038/s44260-025-00038-yModeling the amplification of epidemic spread by individuals exposed to misinformation on social mediaMatthew R. DeVerna0Francesco Pierri1Yong-Yeol Ahn2Santo Fortunato3Alessandro Flammini4Filippo Menczer5Luddy School of Informatics, Computing, and Engineering, Indiana UniversityDepartment of Electronics, Information and Bioengineering, Politecnico di MilanoLuddy School of Informatics, Computing, and Engineering, Indiana UniversityLuddy School of Informatics, Computing, and Engineering, Indiana UniversityLuddy School of Informatics, Computing, and Engineering, Indiana UniversityLuddy School of Informatics, Computing, and Engineering, Indiana UniversityAbstract 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.https://doi.org/10.1038/s44260-025-00038-y |
| spellingShingle | Matthew R. DeVerna Francesco Pierri Yong-Yeol Ahn Santo Fortunato Alessandro Flammini Filippo Menczer Modeling the amplification of epidemic spread by individuals exposed to misinformation on social media npj Complexity |
| title | Modeling the amplification of epidemic spread by individuals exposed to misinformation on social media |
| title_full | Modeling the amplification of epidemic spread by individuals exposed to misinformation on social media |
| title_fullStr | Modeling the amplification of epidemic spread by individuals exposed to misinformation on social media |
| title_full_unstemmed | Modeling the amplification of epidemic spread by individuals exposed to misinformation on social media |
| title_short | Modeling the amplification of epidemic spread by individuals exposed to misinformation on social media |
| title_sort | modeling the amplification of epidemic spread by individuals exposed to misinformation on social media |
| url | https://doi.org/10.1038/s44260-025-00038-y |
| work_keys_str_mv | AT matthewrdeverna modelingtheamplificationofepidemicspreadbyindividualsexposedtomisinformationonsocialmedia AT francescopierri modelingtheamplificationofepidemicspreadbyindividualsexposedtomisinformationonsocialmedia AT yongyeolahn modelingtheamplificationofepidemicspreadbyindividualsexposedtomisinformationonsocialmedia AT santofortunato modelingtheamplificationofepidemicspreadbyindividualsexposedtomisinformationonsocialmedia AT alessandroflammini modelingtheamplificationofepidemicspreadbyindividualsexposedtomisinformationonsocialmedia AT filippomenczer modelingtheamplificationofepidemicspreadbyindividualsexposedtomisinformationonsocialmedia |