A 61-million-person experiment in social influence and political mobilization
Open accessAbstract
Human behaviour is thought to spread through face-to-face social networks, but it is difficult to identify social influence effects in observational studies, and it is unknown whether online social networks operate in the same way. Here we report results from a randomized controlled trial of political mobilization messages delivered to 61 million Facebook users during the 2010 US congressional elections. The results show that the messages directly influenced political self-expression, information seeking and real-world voting behaviour of millions of people. Furthermore, the messages not only influenced the users who received them but also the users' friends, and friends of friends. The effect of social transmission on real-world voting was greater than the direct effect of the messages themselves, and nearly all the transmission occurred between 'close friends' who were more likely to have a face-to-face relationship. These results suggest that strong ties are instrumental for spreading both online and real-world behaviour in human social networks.
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Social networks comprised of friends, family members and co-workers influence individuals' political attitudes and behaviour.
“Social networks comprised of friends, family members and co-workers also influence individuals’ political attitudes and behaviour23,200–202.”
From Cognitive–motivational mechanisms of political polarization in social-communicative contexts (2022) · cites this paper as [200]
The Abstract reports that political mobilization messages influenced users’ voting behaviour and that the effect spread to friends, especially close friends. It also notes observational correlations in turnout among friends, family members and co-workers. The paper does not establish that these networks influence political attitudes, or experimentally show an effect through family members and co-workers. (LLM, conf=0.85)
Experimental — accuracy not yet measuredPolitical messages on social media, as well as faster download speed, can increase information-seeking in the political domain.
“In line with these findings, it has been reported that political messages on social media, as well as faster download speed, can increase information-seeking in the political domain67,71.”
From A systematic review of worldwide causal and correlational evidence on digital media and democracy (2022) · cites this paper as [71]
Facebook's 2010 experiment with 61 million users changed real-world voting behaviour of millions of people for the US midterm elections.
“Back in 2010, a company's experiment with 61 million users [81] resulted in changing the real-world voting behaviour of millions of people for the US midterm elections.”
From Forgetting personal data and revoking consent under the GDPR: Challenges and proposed solutions (2018) · cites this paper as [81]
Bond et al. conducted a 61 million person A/B-style experiment on Facebook to explore how message framing might influence voting behavior.
“For example, Bond et al. conducted a 61 million person A/B-style experiment on Facebook to explore how message framing might influence voting behavior [52].”
From Agile science: creating useful products for behavior change in the real world (2016) · cites this paper as [52]
Many large-scale studies have reported evidence of online ecosystems altering decision-making of crowds and influencing real-world voting of millions of people.
“Many large-scale studies have reported evidence of online ecosystems altering decision-making of crowds [3] and influencing real-world voting of millions of people [4].”
From Influence of augmented humans in online interactions during voting events (2019) · cites this paper as [4]
Online ecosystems acted upon society by altering the emotions and beliefs of large populations of individuals.
“This finding is in full agreement with previous works showing how online ecosystems acted upon society by altering the emotions [19] and beliefs [3, 4] of large populations of individuals.”
From Influence of augmented humans in online interactions during voting events (2019) · cites this paper as [4]
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- Authors
- Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D. I., Marlow, C., Settle, J. E., & Fowler, J. H.
- Affiliations
- University of California San Diego (US)
- Meta (United States) (US)
- Corresponding author
- Fowler, James H.
- Venue
- Nature
- Published
- 2012-09
- Volume
- 489
- Issue
- 7415
- Pages
- 295-298
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC3834737
- OpenAlex
- W2033198212
- Cited by
- 2,575
- References
- 33
- Citations by year
- 2026: 84 · 2025: 114 · 2024: 157 · 2023: 176 · 2022: 193 · 2021: 248 · 2020: 271 · 2019: 204 · 2018: 238 · 2017: 193 · 2016: 214 · 2015: 194 · 2014: 155 · 2013: 110 · 2012: 16
- FWCI
- 486.89
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of General Medical Sciences (P41GM103504)
- Topics
- Social Sciences › Communication › Social Media and Politics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- social influence, political mobilization, Facebook, voting behavior, social transmission, strong ties, weak ties, social networking sites, political communication, information seeking
- MeSH terms
- Humans, Persuasive Communication, Politics, Social Behavior, Sample Size, Internet, Social Networking
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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