Social media sensors as early signals of influenza outbreaks at scale
Detecting early signals of an outbreak in a viral process is challenging due to its exponential nature, yet crucial given the benefits to public health it can provide. If available, the network structure where infection happens can provide rich information about the very early stages of viral outbre...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Springer Science and Business Media LLC
2024
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| Online Access: | https://hdl.handle.net/1721.1/155433 |
| _version_ | 1824457980087631872 |
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| author | Martín-Corral, David García-Herranz, Manuel Cebrian, Manuel Moro, Esteban |
| author2 | Massachusetts Institute of Technology. Institute for Data, Systems, and Society |
| author_facet | Massachusetts Institute of Technology. Institute for Data, Systems, and Society Martín-Corral, David García-Herranz, Manuel Cebrian, Manuel Moro, Esteban |
| author_sort | Martín-Corral, David |
| collection | MIT |
| description | Detecting early signals of an outbreak in a viral process is challenging due to its exponential nature, yet crucial given the benefits to public health it can provide. If available, the network structure where infection happens can provide rich information about the very early stages of viral outbreaks. For example, more central nodes have been used as social network sensors in biological or informational diffusion processes to detect early contagious outbreaks. We aim to combine both approaches to detect early signals of a biological viral process (influenza-like illness, ILI), using its informational epidemic coverage in public social media. We use a large social media dataset covering three years in a country. We demonstrate that it is possible to use highly central users on social media, more precisely high out-degree users from Twitter, as sensors to detect the early signals of ILI outbreaks in the physical world without monitoring the whole population. We also investigate other behavioral and content features that distinguish those early sensors in social media beyond centrality. While high centrality on Twitter is the most distinctive feature of sensors, they are more likely to talk about local news, language, politics, or government than the rest of the users. Our new approach could detect a better and smaller set of social sensors for epidemic outbreaks and is more operationally efficient and privacy respectful than previous ones, not requiring the collection of vast amounts of data. |
| first_indexed | 2024-09-23T09:51:28Z |
| format | Article |
| id | mit-1721.1/155433 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2025-02-19T04:18:36Z |
| publishDate | 2024 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1554332024-12-23T05:24:09Z Social media sensors as early signals of influenza outbreaks at scale Martín-Corral, David García-Herranz, Manuel Cebrian, Manuel Moro, Esteban Massachusetts Institute of Technology. Institute for Data, Systems, and Society Detecting early signals of an outbreak in a viral process is challenging due to its exponential nature, yet crucial given the benefits to public health it can provide. If available, the network structure where infection happens can provide rich information about the very early stages of viral outbreaks. For example, more central nodes have been used as social network sensors in biological or informational diffusion processes to detect early contagious outbreaks. We aim to combine both approaches to detect early signals of a biological viral process (influenza-like illness, ILI), using its informational epidemic coverage in public social media. We use a large social media dataset covering three years in a country. We demonstrate that it is possible to use highly central users on social media, more precisely high out-degree users from Twitter, as sensors to detect the early signals of ILI outbreaks in the physical world without monitoring the whole population. We also investigate other behavioral and content features that distinguish those early sensors in social media beyond centrality. While high centrality on Twitter is the most distinctive feature of sensors, they are more likely to talk about local news, language, politics, or government than the rest of the users. Our new approach could detect a better and smaller set of social sensors for epidemic outbreaks and is more operationally efficient and privacy respectful than previous ones, not requiring the collection of vast amounts of data. 2024-06-28T20:26:24Z 2024-06-28T20:26:24Z 2024-06-17 2024-06-23T03:17:33Z Article http://purl.org/eprint/type/JournalArticle 2193-1127 https://hdl.handle.net/1721.1/155433 Martín-Corral, D., García-Herranz, M., Cebrian, M. et al. Social media sensors as early signals of influenza outbreaks at scale. EPJ Data Sci. 13, 43 (2024). PUBLISHER_CC en 10.1140/epjds/s13688-024-00474-1 EPJ Data Science Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ The Author(s) application/pdf Springer Science and Business Media LLC Springer Berlin Heidelberg |
| spellingShingle | Martín-Corral, David García-Herranz, Manuel Cebrian, Manuel Moro, Esteban Social media sensors as early signals of influenza outbreaks at scale |
| title | Social media sensors as early signals of influenza outbreaks at scale |
| title_full | Social media sensors as early signals of influenza outbreaks at scale |
| title_fullStr | Social media sensors as early signals of influenza outbreaks at scale |
| title_full_unstemmed | Social media sensors as early signals of influenza outbreaks at scale |
| title_short | Social media sensors as early signals of influenza outbreaks at scale |
| title_sort | social media sensors as early signals of influenza outbreaks at scale |
| url | https://hdl.handle.net/1721.1/155433 |
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