Macroscopic patterns of interacting contagions are indistinguishable from social reinforcement
Open accessAbstract
From fake news to innovative technologies, many contagions spread as complex contagions via a process of social reinforcement, where multiple exposures are distinct from prolonged exposure to a single source.1 Contrarily, biological agents such as Ebola or measles are typically thought to spread as simple contagions.2 Here, we demonstrate that these different spreading mechanisms can have indistinguishable population-level dynamics once multiple contagions interact. In the social context, our results highlight the challenge of identifying and quantifying spreading mechanisms, such as social reinforcement,3 in a world where an innumerable amount of ideas, memes and behaviors interact. In the biological context, this parallel allows the use of complex contagions to effectively quantify the non-trivial interactions of infectious diseases.
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Machine learning analysis of online user behavior revealed extraordinary activity by automated agents called social bots.
“By using state-of-the-art machine learning techniques to analyze the online behavior of users (see Methods for details), we have discovered an extraordinary activity of automated agents, referred to as social bots 14,15,35,39 .”
From Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics (2020) · cites this paper as [39]
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References 43 · Cited by 71
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- Authors
- Hébert-Dufresne, L., Scarpino, S. V., & Young, J.-G.
- Affiliations
- Northeastern University (US)
- University of Michigan (US)
- Venue
- Nature Physics
- Published
- 2020-02-24
- Volume
- 16
- Issue
- 4
- Pages
- 426-431
- ISSN
- 1745-2473 (ISSN-L)
- 1745-2481
- PMC
- PMC8247125
- OpenAlex
- W2948157569
- Cited by
- 71
- References
- 43
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 12 · 2023: 11 · 2022: 9 · 2021: 15 · 2020: 11
- FWCI
- 4.86
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Physics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (DMS-1829826)
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (1P20 GM125498-01)
- James S. McDonnell Foundation
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Mathematical and Theoretical Epidemiology and Ecology Models
- Keywords
- complex contagion, simple contagion, social reinforcement, multiple exposures, infectious disease transmission, fake news spread
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