The DOI matches its Crossref record; 32 of the 33 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Granovetter, M. S. (1973) · American Journal of Sociology · doi:10.1086/225469 · Cited by 40,491
Ellison, N. B., Steinfield, C., & Lampe, C. (2007) · Journal of Computer-Mediated Communication · doi:10.1111/j.1083-6101.2007.00367.x · Cited by 9,939
The Spread of Obesity in a Large Social Network over 32 Years
Christakis, N. A., & Fowler, J. H. (2007) · New England Journal of Medicine · doi:10.1056/nejmsa066082 · Cited by 5,007
Self-citationNetwork Analysis in the Social Sciences
Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009) · Science · doi:10.1126/science.1165821 · Cited by 4,280
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., et al. (2009) · Science · doi:10.1126/science.1167742 · Cited by 3,365
Self-citationThe spread of behavior in an online social network experiment
Centola, D. (2010) · Science · doi:10.1126/science.1185231 · Cited by 2,692
The collective dynamics of smoking in a large social network
Christakis, N. A., & Fowler, J. H. (2008) · New England Journal of Medicine · doi:10.1056/nejmsa0706154 · Cited by 2,390
Self-citationExperimental Study of Inequality and Unpredictability in an Artificial Cultural Market
Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006) · Science · doi:10.1126/science.1121066 · Cited by 2,231
A simple rule for the evolution of cooperation on graphs and social networks
Ohtsuki, H., Hauert, C., Lieberman, E., & Nowak, M. A. (2006) · Nature · doi:10.1038/nature04605 · Cited by 2,208
Fowler, J. H., & Christakis, N. A. (2008) · BMJ · doi:10.1136/bmj.a2338 · Cited by 1,990
Self-citation
Cited by
Sorted by citations
Using social and behavioural science to support COVID-19 pandemic response
Bavel, J. J. V., Baicker, K., Boggio, P. S., Capraro, V., Cichocka, A., Cikara, M., Crockett, M. J., Crum, A. J., Douglas, K. M., Druckman, J. N., et al. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0884-z · Cited by 4,964
Self-citationExperimental evidence of massive-scale emotional contagion through social networks
Kramer, A. D. I., Guillory, J. E., & Hancock, J. T. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1320040111 · Cited by 3,687
Self-citationBig other: Surveillance Capitalism and the Prospects of an Information Civilization
Zuboff, S. (2015) · Journal of Information Technology · doi:10.1057/jit.2015.5 · Cited by 3,205
“Whenever these experiments have been revealed, however, they have ignited fi erce public debate (Bond et al. , 2012; Flynn, 2014; Gapper, 2014; Goel, 2014; Kramer et al. , 2014; Lanier, 2014; Zittrain, 2014).”
Newman, M. E. J. (2018) · doi:10.1093/oso/9780198805090.001.0001 · Cited by 2,453
Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning
Yarkoni, T., & Westfall, J. (2017) · Perspectives on Psychological Science · doi:10.1177/1745691617693393 · Cited by 2,088
Metadata
- 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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
