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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In the citing paper
“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]
Citations
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Research works printed but not linked: 3
- Anderson L. R., Holt C. A.Information cascades in the laboratory, Am. Econ. Rev. 87, 847 (1997).
- B. Gu M. Lin “The dynamics of online consumer reviews ” Workshop on Information Systems and Economics (WISE) (2006).
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Cited by
Sorted by citations
Massanari, A. (2017) · New Media & Society · doi:10.1177/1461444815608807 · Cited by 1,264
“As other scholars have noted, such a system can create “herding” or power law effects around particular material, biasing individuals to mirror the voting behavior of others (Muchnik et al., 2013).”
BackgroundSuddaby, R., Bitektine, A. B., & Haack, P. (2017) · Academy of Management Annals · doi:10.5465/annals.2015.0101 · Cited by 1,247
The “Macro” and the “Micro” of Legitimacy: Toward a Multilevel Theory of the Legitimacy Process
Bitektine, A. B., & Haack, P. (2014) · Academy of Management Review · doi:10.5465/amr.2013.0318 · Cited by 1,098
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763
“To do so, the gold standard are randomized controlled experiments [Aral and Walker 2011; Bakshy et al. 2012; Muchnik et al. 2013], but when experimentation is impractical or violates ethical standards, many studies resort to observational data collected from social platforms.”
BackgroundNetwork analysis reveals open forums and echo chambers in social media discussions of climate change
Williams, H. T. P., McMurray, J. R., Kurz, T., & Hugo Lambert, F. (2015) · Global Environmental Change · doi:10.1016/j.gloenvcha.2015.03.006 · Cited by 631
“and Kotcher, 2009), while social network influence is known to affect individual attitudes and behaviours in general (Katz and Lazarsfeld, 1955; Katz, 1957; Salganik et al., 2006; Bond et al., 2012; Centola, 2010; Wu et al., 2011; Muchnik et al., 2013; Kramer et al., 2014).”
BackgroundInfluential
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
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