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.
“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
The gold standard for causal analyses are randomized controlled experiments.
“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
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 [3]
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 [3]
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BackgroundReverse Engineering Socialbot Infiltration Strategies in Twitter
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Women’s visibility in academic seminars: Women ask fewer questions than men
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“Such social influence biases have been found in online interactions, where, for example, the tone of the first comment posted influences the tone of subsequent comments [26].”
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“…rely primarily on evaluating differences in means and t tests, Kramer et al. (2014) utilize Poisson regression and weighted linear regression, and Muchnik et al. (2013) use more complex (but still well known) regression models that include random effects to account for repeat observations from…”
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“This behavior can be explained with processes like social influence bias, which can lead to herding effects (Muchnik et al., 2013), or, take what psychologists call “mirroring” (Chartrand & Bargh, 1999), that describes the process of unconsciously imitating other people.”
BackgroundDigital Media and Political Participation
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BackgroundResultThe Anatomy of Reddit: An Overview of Academic Research
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“[44] performed a large-scale experiment on a Reddit-like platform to study the herding effect of social influence and how the sys-”
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BackgroundThe strength of long-range ties in population-scale social networks
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“Research on other platforms shows evidence for such a reinforcement effect (Muchnik et al. 2013).”
BackgroundResearch collaboration experiences, good and bad: Dispatches from the front lines
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“8 The second incentive is the reviewer’s social incentive that may generate a positive or negative correlation across ratings (Muchnik et al. 2013).”
Behavioral biases in marketing
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Social Simulation in the Social Sciences
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“More recently, Muchnik, Aral, and Taylor (2013) performed a large-scale experiment on a social news aggregation website to study biases in the rating behavior of users.”
BackgroundLeveraging position bias to improve peer recommendation
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BackgroundResultTransaction cost economics in the digital economy: A research agenda
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“Additional sources of bias can stem from social influence due to anchoring (Muchnik et al., 2013), consumer characteristics (Zhu and Zhang, 2010), or disagreement among prior reviews (Nagle and Riedl, 2014).”
Rate or Trade? Identifying Winning Ideas in Open Idea Sourcing
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“(Muchnik et al. 2013).”
Learning from Mixed Signals in Online Innovation Communities
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“Theories on collective intelligence suggest that individual evaluations should remain independent to avoid bias from social in fl uence (Lorenz et al. 2011, Muchnik et al. 2013).”
BackgroundSocial Data: Biases, Methodological Pitfalls, and Ethical Boundaries
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BackgroundUser-Generated Content and Competing Firms’ Product Design
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A methodology for learning, analyzing, and mitigating social influence bias in recommender systems
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Business Analytics: Radical Shift or Incremental Change?
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The Next Frontiers of Digital Innovation Research
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MethodConsumers and Curators: Browsing and Voting Patterns on Reddit
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“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
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BackgroundThe outcome of online social interactions on Facebook pages
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The wisdom of smaller, smarter crowds
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Knowledge acquisition: A Complex networks approach
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Health Misinformation and the Power of Narrative Messaging in the Public Sphere
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- 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
- 871
- References
- 65
- Citations by year
- 2026: 65 · 2025: 51 · 2024: 52 · 2023: 38 · 2022: 61 · 2021: 68 · 2020: 71 · 2019: 76 · 2018: 78 · 2017: 96 · 2016: 72 · 2015: 65 · 2014: 62 · 2013: 13 · 2012: 3
- FWCI
- 50.46
- 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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