The spread of behavior in an online social network experiment
Abstract
How do social networks affect the spread of behavior? A popular hypothesis states that networks with many clustered ties and a high degree of separation will be less effective for behavioral diffusion than networks in which locally redundant ties are rewired to provide shortcuts across the social space. A competing hypothesis argues that when behaviors require social reinforcement, a network with more clustering may be more advantageous, even if the network as a whole has a larger diameter. I investigated the effects of network structure on diffusion by studying the spread of health behavior through artificially structured online communities. Individual adoption was much more likely when participants received social reinforcement from multiple neighbors in the social network. The behavior spread farther and faster across clustered-lattice networks than across corresponding random networks.
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This rapid attenuation contrasts with findings in human networks, where social influence can propagate across multiple degrees of separation.
“This rapid attenuation contrasts with findings in human networks, where social influence can propagate across multiple degrees of separation 25,27 .”
From The Moltbook Illusion: Separating Human Influence from Emergent Behavior in AI Agent Societies (2026) · cites this paper as [25]
The web has revolutionised the practice of public health surveillance.
“While conventional surveillance was based on health data from healthcare organisations such as hospitals, government databases and insurance companies, the web has revolutionised the practice of PHS.7”
From Purpose-oriented review of public health surveillance systems: use of surveillance systems and recent advances (2024) · cites this paper as [7]
An individual is much more likely to adopt a content when his/her neighbors in the social network tend to reinforce it.
“The analysis of information and behavioral spreading on social media [17] revealed that an individual is much more likely to adopt a content when his/her neighbors in the social network tend to reinforce it [48].”
From Influence of augmented humans in online interactions during voting events (2019) · cites this paper as [48]
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- Authors
- Centola, D.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Centola, Damon
- ORCID
- Centola, Damon
- Venue
- Science
- Published
- 2010-09-03
- Volume
- 329
- Issue
- 5996
- Pages
- 1194-1197
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2150208547
- Cited by
- 2,692
- References
- 27
- Citations by year
- 2026: 158 · 2025: 176 · 2024: 130 · 2023: 127 · 2022: 177 · 2021: 245 · 2020: 192 · 2019: 177 · 2018: 202 · 2017: 208 · 2016: 178 · 2015: 202 · 2014: 197 · 2013: 136 · 2012: 115
- FWCI
- 72.99
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- ScholarlyCommons (University of Pennsylvania) (submittedVersion)
- 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
- Social Sciences › Decision Sciences › Management Science and Operations Research › Innovation Diffusion and Forecasting
- Keywords
- social networks, network structure, clustering, social reinforcement, online communities, health behavior adoption, network diameter
- MeSH terms
- Female, Humans, Interpersonal Relations, Male, Reinforcement, Psychology, Social Support, Health Behavior, Internet
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