The DOI matches its Crossref record; 56 of the 65 references it cites were found in a registry or an index.
Social Influence Bias: A Randomized Experiment
Abstract
Follow the Leader?: The Internet has increased the likelihood that our decisions will be influenced by those being made around us. On the one hand, group decision-making can lead to better decisions, but it can also lead to “herding effects” that have resulted in financial disasters. Muchnik et al. (p. 647 ) examined the effect of collective information via a randomized experiment, which involved collaboration with a social news aggregation Web site on which readers could vote and comment on posted comments. Data were collected and analyzed after the Web site administrators arbitrarily voted positively or negatively (or not at all) as the first comment on more than 100,000 posts. False positive entries led to inflated subsequent scores, whereas false negative initial votes had small long-term effects. Both the topic being commented upon and the relationship between the poster and commenter were important. Future efforts will be needed to sort out how to correct for such effects in polls or other collective intelligence systems in order to counter social biases.
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“prior ratings and reviews introduce significant bias in individual rating behavior and writing style, creating a herding effect”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Muchnik et al., 2013
In the citing paper
“The gold standard for such causal analyses are randomized controlled experiments (Aral and Walker, 2011; Muchnik et al., 2013).”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Muchnik et al., 2013
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 [3]
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 [3]
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“…privilege some material over others, shaping knowledge and behaviors (Benkler, 2016; Tufecki, 2016; for examples, see Kramer, Guillory, and Hancock, 2014, Muchnik et al. 2013, and Bond et 7 https://blog.mozilla.org/blog/2016/02/08/the-internet-is-a-global-public-resource/ al., 2012).”
BackgroundUser-Generated Content and Competing Firms’ Product Design
Kwark, Y., Chen, J., & Raghunathan, S. (2017) · Management Science · doi:10.1287/mnsc.2017.2839 · Cited by 90
A methodology for learning, analyzing, and mitigating social influence bias in recommender systems
Krishnan, S., Patel, J. M., Franklin, M. J. M., & Goldberg, K. (2014) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/2645710.2645740 · Cited by 89
“[28], they found that when they positively treated posts with higher up-vote counts it lead to a significant increase in the likelihood of additional up votes (32% more likely).”
BackgroundInfluentialKaraman, H. (2020) · Management Science · doi:10.1287/mnsc.2020.3758 · Cited by 81
Business Analytics: Radical Shift or Incremental Change?
Gillon, K., Aral, S., Lin, C., Mithas, S., & Zozulia, M. (2014) · Communications of the Association for Information Systems · doi:10.17705/1cais.03413 · Cited by 88
Self-citationTan, T. F., Netessine, S., & Hitt, L. (2017) · Information Systems Research · doi:10.1287/isre.2017.0712 · Cited by 81
“Since user ratings may suffer from various social influence biases (e.g.,Li and Hitt, 2008; Moe and Trusov, 2011; Muchnik et al., 2013), we refrain from making causal inference of the user rating and movie quality.”
Li, X. (2018) · Information Systems Research · doi:10.1287/isre.2017.0728 · Cited by 85
Reciprocity of social influence
Mahmoodi, A., Bahrami, B., & Mehring, C. (2018) · Nature Communications · doi:10.1038/s41467-018-04925-y · Cited by 102
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Co-dynamics of climate policy stringency and public support
Konc, T., Drews, S., Savin, I. V., & van den Bergh, J. C. J. M. (2022) · Global Environmental Change · doi:10.1016/j.gloenvcha.2022.102528 · Cited by 86
The Next Frontiers of Digital Innovation Research
Yoo, Y., Henfridsson, O., Kallinikos, J., Gregory, R. W., Burtch, G., Chatterjee, S., & Sarker, S. (2024) · Information Systems Research · doi:10.1287/isre.2024.editorial.v35.n4 · Cited by 90
Li, H., Zhang, Z., Meng, F., & Janakiraman, R. (2017) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2017.08.003 · Cited by 83
Emoticons vs. Emojis on Twitter: A Causal Inference ApproachPreprint
Pavalanathan, U., & Eisenstein, J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.08480 · Cited by 82
“Although causal inference framework has been widely used in fields such as epidemiology and political science to design and analyze randomized experiments, this approach was rare in social media research until recently (Muchnik, Aral, and Taylor 2013; King, Pan, and Roberts 2014).”
MethodConsumers and Curators: Browsing and Voting Patterns on Reddit
Glenski, M., Pennycuff, C., & Weninger, T. (2017) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2017.2742242 · Cited by 77
“Earlier work in this area has found significant influence effects [5], [6], [29] on a user’s vote due to ranking bias [14], but we make no such claim here.”
BackgroundA new sociology of humans and machines
Tsvetkova, M., Yasseri, T., Pescetelli, N., & Werner, T. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02001-8 · Cited by 122
“Crowds perform better when individual opinions are either independent or diverse 132 , whereas social interaction can hinder 133–135 or improve 136,137 collective performance.”
Leavitt, A., & Clark, J. (2014) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2557140 · Cited by 84
“We must also keep in mind that attention drives much of what is produced and mediated in social news sites: as Muchnik, Aral, and Taylor show [23], even one initial upvote in a social news platform can lead to a strong and consequential social influence bias.”
BackgroundThe outcome of online social interactions on Facebook pages
Khobzi, H., Lau, R. Y. K., & Cheung, T. C.-H. (2018) · Internet Research · doi:10.1108/intr-04-2017-0161 · Cited by 79
The wisdom of smaller, smarter crowds
Goldstein, D. G., McAfee, R. P., & Suri, S. (2014) · ACM Conference on Economics and Computation (EC) · doi:10.1145/2600057.2602886 · Cited by 76
Knowledge acquisition: A Complex networks approach
de Arruda, H. F., Silva, F. N., Costa, L. D. F., & Amancio, D. R. (2017) · Information Sciences · doi:10.1016/j.ins.2017.08.091 · Cited by 75
Health Misinformation and the Power of Narrative Messaging in the Public Sphere
Caulfield, T., Marcon, A. R., Murdoch, B., Brown, J. M., Perrault, S. T., Jarry, J., Snyder, J., Anthony, S. J., Brooks, S. P., Master, Z., et al. (2019) · Canadian Journal of Bioethics · doi:10.7202/1060911ar · Cited by 82
“The clustering of individuals online into various communities highlights the role that algorithms as well as public intellectuals, celebrities, or influencers can play in knowledge transmission [38-42].”
BackgroundSaifee, D. H., Zheng, Z., Bardhan, I. R., & Lahiri, A. (2020) · Information Systems Research · doi:10.1287/isre.2020.0945 · Cited by 81
Metadata
- Authors
- Muchnik, L., Aral, S., & Taylor, S. J.
- Affiliations
- Hebrew University of Jerusalem (IL)
- Massachusetts Institute of Technology (US)
- New York University (US)
- Corresponding author
- Aral, Sinan
- Venue
- Science
- Published
- 2013-08-09
- Volume
- 341
- Issue
- 6146
- Pages
- 647-651
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2063248556
- Cited by
- 892
- References
- 65
- Citations by year
- 2026: 100 · 2025: 60 · 2024: 52 · 2023: 39 · 2022: 60 · 2021: 61 · 2020: 69 · 2019: 72 · 2018: 78 · 2017: 96 · 2016: 74 · 2015: 65 · 2014: 53 · 2013: 12
- FWCI
- 39.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Keywords
- herding behavior, online ratings, collective intelligence, collective judgment, decision-making
- MeSH terms
- Decision Making, Humans, Intelligence, Politics, Public Opinion, Social Environment, Bias, Focus Groups, Internet, Behavior Control, Friends
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.
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