https://doi.org/10.1140/epjds/s13688-024-00474-1
Research
Social media sensors as early signals of influenza outbreaks at scale
1
Department of Mathematics and GISC, Universidad Carlos III de Madrid, 28911, Leganes, Spain
2
Institute for Data, Systems, and Society, Massachusetts Institute of Technology, 02139, Cambridge, MA, USA
3
UNICEF, New York City, USA
4
Center for Automation and Robotics, Spanish National Research Council, Madrid, Spain
5
Network Science Institute, Northeastern University, 02115, Boston, MA, USA
6
Department of Telematics and Computing, ICAI Engineering School, Universidad Pontificia Comillas, 28015, Madrid, Spain
Received:
20
April
2023
Accepted:
19
April
2024
Published online:
17
June
2024
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.
Key words: Computational epidemiology / Social networks / Informational epidemics / Biological epidemics
© The Author(s) 2024
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