Cited by 892Opinion Dynamics and Social InfluenceDecision-Making and Behavioral EconomicsMobile Crowdsensing and Crowdsourcing

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

Muchnik, Lev, Aral, Sinan, Taylor, Sean J.·2013·Science

DOI 10.1126/science.1240466

Open this paper in CoChat — free account

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.

Integrity signals

Registry

Crossref · 10.1126/science.1240466

Cites

56 of 56 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
Judgment under Uncertainty: Heuristics and Biases: Biases in judgments reveal some heuristics of thinking under uncertainty.
Tversky, A., & Kahneman, D. (1974) · Science · doi:10.1126/science.185.4157.1124 · Cited by 28,810
Judgment under Uncertainty: Heuristics and Biases
Tversky, A., & Kahneman, D. (1975) · Utility, Probability, and Human Decision Making · doi:10.1007/978-94-010-1834-0_8 · Cited by 22,650
Inference from iterative simulation using multiple sequences
Gelman, A., & Rubin, D. B. (1992) · Statistical Science · doi:10.1214/ss/1177011136 · Cited by 16,627
Social Theory and Social Structure
Ross, E. J., & Merton, R. Κ. (1958) · The American Catholic Sociological Review · doi:10.2307/3709394 · Cited by 10,318
The Kolmogorov-Smirnov Test for Goodness of Fit
Massey, F. J. (1951) · Journal of the American Statistical Association · doi:10.1080/01621459.1951.10500769 · Cited by 7,210
A Simple Model of Herd Behavior
Banerjee, A. V. (1992) · The Quarterly Journal of Economics · doi:10.2307/2118364 · Cited by 6,556
Identification of Endogenous Social Effects: The Reflection Problem
Manski, C. F. (1993) · The Review of Economic Studies · doi:10.2307/2298123 · Cited by 6,209
The Effect of Word of Mouth on Sales: Online Book Reviews
Chevalier, J. A., & Mayzlin, D. (2006) · Journal of Marketing Research · doi:10.1509/jmkr.43.3.345 · Cited by 6,003

Flagged first, then by citations

Show all 65 references

9 of the 65 references couldn’t be matched to a known paper.

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).
S. E. Asch in Groups Leadership and Men H. Guetzkow Ed. (Carnegie Press Pittsburgh PA 1951) pp. 177–190.

Sorted by citations

Items 501–600 of 873

Empirical analysis of the user reputation and clustering property for user-object bipartite networks
Liu, X., Jia, S., & Gu, Y. (2019) · International Journal of Modern Physics C · doi:10.1142/s0129183119500359 · Cited by 5
Sequential Voting Promotes Collective Discovery in Social Recommendation Systems
Celis, L. E., Krafft, P. M., & Kobe, N. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v10i1.14755 · Cited by 14
Concentration and Unpredictability of Forecasts in Artificial Investment Games
Xiu, C., Hong, F., & Zhao, X. (2016) · Experimental Economics · doi:10.1007/s10683-019-09632-z · Cited by 5
Impact of social influence on adoption behavior
Sarkar, S., Aleali, A., Shakarian, P., Armenta, M., Sanchez, D. N., & Lakkaraju, K. (2019) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/3341161.3342882 · Cited by 5
“This includes revising their initial opinions by processing and averaging the different influences acting on them [14] and social information provides one mechanism through which this is achieved.”
Background
“Trust Me, I Have a Ph.D.”: A Propensity Score Analysis on the Halo Effect of Disclosing One's Offline Social Status in Online Communities
Park, K., Kwak, H., Song, H., & Cha, M. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7321 · Cited by 8
“Social influence and pre-existing records can also bias individuals’ behavior when judging content quality (Muchnik, Aral, and Taylor 2013; Aral 2014; Berry and Taylor 2017).”
Background
What Drives Performance Measurement in NGOs?: A Case Study from Pakistan
Tahajuddin, S., Hasan, S. S., & Kassim, A. W. M. (2021) · Malaysian Journal of Social Sciences and Humanities (MJSSH) · doi:10.47405/mjssh.v6i9.1025 · Cited by 5
Institutional dynamics and learning networks
Poon, P., Flack, J. C., & Krakauer, D. C. (2022) · PLoS ONE · doi:10.1371/journal.pone.0267688 · Cited by 5
“Recently the use of news aggregation sites on the web has instantiated literal digital containers of public opinion supporting both consensus formation scores and a tracking of histories contributing to new forms of collective herding dynamics [47].”
Background
Manipulation: Online Platforms' Inescapable Fate
Lee, S. Y., Qiu, L., & Whinston, A. B. (2014) · International Conference on Information Systems · Cited by 6
Effects of dyadic communication on race-based impressions and memory
Biernat, M., Villicana, A. J., Sesko, A. K., & Zhao, X. (2016) · Group Processes & Intergroup Relations · doi:10.1177/1368430216663022 · Cited by 5
The Impacts of Online Lightweight Interactions as Signals
Wang, J., & Chang, C.-H. (2013) · Cited by 5
Is Tom Cruise Threatened? An Empirical Study of the Impact of Product Variety on Demand ConcentrationPreprint
Tan, T. F., Netessine, S., & Hitt, L. M. (2012) · SSRN Electronic Journal · doi:10.2139/ssrn.2188354 · Cited by 5
Optimal Experimental Design for Staggered RolloutsPreprint
Xiong, R., Athey, S. C., Bayati, M., & Imbens, G. W. (2019) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.1911.03764 · Cited by 5
“Large technology companies run thousands or even tens of thousands of experiments per year to evaluate the impact of various interventions, design better products, and understand mechanisms (Muchnik et al. 2013, Aral and Walker 2014, Brynjolfsson et al. 2018, 2019, Salganik 2019).”
Background
The Bright Side of Lower Quality: Evidence from Restaurant ExplorationPreprint
Carrera, C., Martínez‐de‐Albéniz, V., & Sosa, M. E. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3628270 · Cited by 5
Age Discrimination in Hiring: An Experimental Study in ItalyPreprint
Dormidontova, Y., Castellani, M., & Squazzoni, F. (2020) · doi:10.31235/osf.io/95ny7 · Cited by 5
Consumer and AI Co-creation: When and Why Human Participation Improves AI Creation.Preprint
Zhang, M., Sun, T., Luo, L., & Golden, J. H. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3929070 · Cited by 5
The Causal Effect of Attention and Recognition on the Nature of User-Generated Content: Experimental Results from an Image-Sharing Social NetworkPreprint
Huang, J. T., Kaul, R., & Narayanan, S. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4185208 · Cited by 4
Culling the Herd: Using Real-World Randomized Experiments to Measure Social Bias with Known Costly GoodsPreprint
de Matos, M. G., Ferreira, P., Smith, M. D., & Telang, R. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2336158 · Cited by 2
Novelty in Content Creation: Experimental Results Using Deep Learning–Based Image Recognition on a Large Social Network
Huang, J. T., Kaul, R., & Narayanan, S. (2025) · Marketing Science · doi:10.1287/mksc.2023.0547 · Cited by 4
Causality and its applications in social media: A survey
Liu, T., & Zhao, S. (2014) · doi:10.13328/j.cnki.jos.004724 · Cited by 4
“Aral 等人把 Facebook 上的 140 万朋友关系作为研究对象 , 使用随机对照实验的方法研究这些朋友关系所实施的同伴影响对同伴在某些产品使用上的因果作用 [16] , 并且于 2013 年使用随机对照实验的方法研究社会媒体上的已有的用户投票结果对于后来者给出好评或坏评的因果作用 [44] .”
A promotive structural balance model based on reinforcement learning for signed social networks
Yang, M., Wang, X., Ma, L., et al. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07298-y · Cited by 4
“relationships are widespread in signed social networks, which would break inherent relationships among countries, parties or people, leading to social and political upheavals [6].”
Background
Areas of influence in influencer marketing. To what extent is the communication under brand control?
Łaszkiewicz, A. (2022) · Marketing of Scientific and Research Organizations · doi:10.2478/minib-2022-0018 · Cited by 4
The effect of wage proposals on efficiency and income distribution
Ezquerra, L., Gómez‐Miñambres, J., Jiménez, N., et al. (2023) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2023.10.006 · Cited by 4
Conditions Facilitating the Aversion of Unpopular Norms: An Agent-Based Simulation Study
Zareen, Z., Zafar, M., & Zia, K. (2016) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2016.070769 · Cited by 4
Studying human social environment and state with social network data
Kolonin, A. G. (2016) · doi:10.1109/csgb.2016.7587680 · Cited by 4
Self-correcting dynamics in social influence processes
van de Rijt, A. (2022) · Edward Elgar Publishing eBooks · doi:10.4337/9781789909432.00033 · Cited by 4
A robust reputation iterative algorithm based on Z-statistics in a rating system with thorny objects
Zhu, H., Xiao, Y., Wang, Z., et al. (2022) · Journal of the Operational Research Society · doi:10.1080/01605682.2022.2101952 · Cited by 4
“As the availability of online alternative objects expands, Internet users rely increasingly heavily on objects ’ recommendations and evaluations (Durbach & Montibeller, 2019; Muchnik et al., 2013) when making decisions.”
Background
Brain dynamics of recommendation-based social influence on preference change: A magnetoencephalography study
Irani, F., Maunula, S., Muotka, J. S., Leppäniemi, M., Kukkonen, M., Monto, S., & Parviainen, T. M. (2022) · Social Neuroscience · doi:10.1080/17470919.2022.2126001 · Cited by 4
“…increase pro-environmental behavior (Gugenishvili et al., 2021), health-related behavior (Templeton et al., 2016), and, related to the current study, consumer behavior, where product ratings and reviews from others are shown to significantly affect preferences and behavior (Muchnik et al., 2013).”
Background
Social Influence in Signed Networks
He, X., Lu, J., Du, H., & Jin, X. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3220944 · Cited by 5
A model for herd behaviour based on a spatial public goods game
Lima, J. A., & Schimit, P. H. T. (2023) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2023.128897 · Cited by 4
Navigating the Stars: The Moderating Effect of Social Media Usage on the Admission Intention of B-schools
Siji, S., & Parsad, C. (2023) · FIIB Business Review · doi:10.1177/23197145231185391 · Cited by 4
“Several studies have been conducted on these concerns (Jacobsen, 2015; Mayzlin et al., 2014; Moe & Michael, 2011; Muchnik et al .”
Why do high-tech firms offer perks at work?
Ye, X., & Tambe, P. B. (2015) · International Conference on Information Systems · Cited by 4
Sequential sampling, magnitude estimation, and the wisdom of crowds
Nash, U. W. (2017) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2017.01.001 · Cited by 6
The Impact of Group Size on the Discovery of Hidden Profiles in Online Discussion Groups
Tausczik, Y., & Huang, X. (2018) · ACM Transactions on Social Computing · doi:10.1145/3359758 · Cited by 6
“We chose 4 as the smallest group size because it is a typical group size used in prior studies of traditional small groups [27] and 32 was the largest group size practical for an experiment.”
BackgroundMethod
Conspiratorial Beliefs Observed through Entropy Principles
Golo, N., & Galam, S. (2015) · Entropy · doi:10.3390/e17085611 · Cited by 4
“Muchnik et al in [17], who discovered that prior ratings create significant bias in individual rating behavior, and positive and negative social influences created asymmetric effects.”
Background
The influence of influence: The effect of task repetition on persuaders and persuadees
Chesney, T., Chuah, S., Hoffmann, R. F., et al. (2016) · Decision Support Systems · doi:10.1016/j.dss.2016.10.001 · Cited by 4
“If another person immediately tries to influence them again, does the fact that they have already been persuaded mean that they will be more likely to be persuaded the second time, or less? Or will there be no difference? What about the person doing the persuading: will they be more likely to influence someone if they…”
Background
Design of Randomized Experiments in NetworksPreprint
Walker, D., & Muchnik, L. (2014) · SSRN Electronic Journal · doi:10.2139/ssrn.2477076 · Cited by 4
Self-citation
Influentials, Imitables or Susceptibles? Virality and Word-of-Mouth Conversations in Online Social NetworksPreprint
Susarla, A., Oh, J., & Tan, Y. (2012) · SSRN Electronic Journal · doi:10.2139/ssrn.1984690 · Cited by 4
“Propensity to engage in WOM Emotional triggers Identity signaling [10], interpersonal relationships, Emotional cues [49] Influence in triggering WOM”
A Tree-Based Approach for Addressing Self-Selection in Impact Studies with Big DataPreprint
Yahav, I., Galit Shmueli, A., & Mani, D. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2276770 · Cited by 4
The Value of Price Discrimination in Large Random NetworksPreprint
Huang, J., Mani, A., & Wang, Z. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3368458 · Cited by 4
The Role of Randomized Control Trials in Online Demand Generation: Exploratory Evidence from FacebookPreprint
Runge, J., & Nair, H. S. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3794028 · Cited by 3
Proceedings of The ACM Collective Intelligence Conference
(2023) · doi:10.1145/3582269 · Cited by 4
Personalization, Engagement, and Content Quality on Social Media: An Evaluation of Reddit's News FeedPreprint
Moehring, A. (2024) · doi:10.31219/osf.io/8yuwe · Cited by 4
Do cryptocurrency rewards improve platform valuations?
Subramanian, H., & Rouxelin, F. (2024) · Information & Management · doi:10.1016/j.im.2024.103989 · Cited by 3
The Influence of Social Media-like Cues on Visual Attention—An Eye-Tracking Study with Food Products
Mamalikou, M., Gkatzionis, K., & Παναγιώτου, Μ. (2025) · Journal of Eye Movement Research · doi:10.3390/jemr18060062 · Cited by 3
“In digital environments, such cues have been shown to disproportionately direct attention and affect engagement behaviors [79,80].”
Asymptotic optimality of myopic optimization in trial-offer markets with social influence
Abeliuk, A., Berbeglia, G., Maldonado, F., & Hentenryck, P. V. (2016) · Minerva Access (University of Melbourne) · Cited by 4
Beyond dislike counts: How YouTube users react to the visibility of social cues
Zhang, M. M., & Ng, Y. M. M. (2024) · New Media & Society · doi:10.1177/14614448241287510 · Cited by 3
“External influences of social cues play a significant role in shaping opinion formation, often leading to phenomena like “herding behavior” (Muchnik et al., 2013; Zhang and Ng, 2023).”
Influential
Impact of rounded rating display and confidence cues on the subsequent reviews
Liu, C., & Kim, K. (2025) · European Journal of Information Systems · doi:10.1080/0960085x.2025.2471074 · Cited by 3
“In regard to these, the importance of social influence has been widely reported (Y.-J. Lee et al., 2015; Ma et al., 2013; Moe & Trusov, 2011; Muchnik et al., 2013; Schlosser, 2005; Sridhar & Srinivasan, 2012; Wang et al., 2018).”
Influential
Bias in online reviews: The roles played by consumers and products in reviewing online games
Jinna, P., & Chengalur-Smith, I. N. (2025) · Information & Management · doi:10.1016/j.im.2025.104141 · Cited by 3
Understanding the Effects of AI-based Credibility Indicators When People Are Influenced By Both Peers and Experts
Lu, Z., Li, P., Wang, W., & Yin, M. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713871 · Cited by 5
“It plays a crucial role in the formation of opinions, and may explain critical phenomena like herding and the band-wagon effect [32, 77].”
Health behavior spreading with similar diminishing returns effect
Zhang, Y., Tang, S., Pei, S., et al. (2015) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2014.12.035 · Cited by 3
Asymmetric Recommendations
Nov, O., & Arazy, O. (2015) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/2792838.2799667 · Cited by 3
“Some supporting evidence in this direction has been provided in prior research [14].”
BackgroundResult
Bubble Studies: The Brass Tacks
Hendricks, V. F. (2016) · Bloomsbury Academic eBooks · doi:10.5040/9781472597717.ch-006 · Cited by 4
“…(Peng 1994; Layman et al. 2006); (d) appreciating the same art; (e) “liking” the same posts on social media (Centola 2010); (f) upvoting the same reviews (Muchnik et al 2013); (g) purchasing the same brand names; (h) subscribing to the same research program in science (Budtz Pedersen & Hendricks…”
Background
Infectivity enhances prediction of viral cascades in Twitter
Li, W., Cranmer, S., Zheng, Z., & Mucha, P. J. (2019) · PLoS ONE · doi:10.1371/journal.pone.0214453 · Cited by 3
Ranking-based rich-get-richer processes
Analytis, P. P., Gelastopoulos, A., & Stojić, H. (2023) · The Annals of Applied Probability · doi:10.1214/22-aap1921 · Cited by 3
“Even for these algorithms, however, ranking-based rich-get-richer dynamics can be at play if a link’s actual or perceived utility for the users depends on the object’s popularity [4, 47].”
Background
Social Influence Leads to the Formation of Diverse Local Trends
Epstein, Z., Groh, M., Dubey, A., & Pentland, A. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479553 · Cited by 6
“Social influence occurs when the decisions of a user are impacted by those of other users [10], and has been shown to be a key design dimension for contexts as varied as health behavior [9], political engagement [5] , collective behavior [41], online book purchasing [8], food ordering [30], and digital news engagement…”
Background
The fake news propagate with more self-loop in online social networks
Zhao, Z., Zhu, X., Han, T., & Chang, C. (2021) · IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) · doi:10.1109/iciba52610.2021.9688320 · Cited by 3
Improving the Performance of Reputation Evaluation by Combining a Network Structure With Nonlinear Recovery
Li, M., Han, C., Jiang, Y., & Di, Z. (2022) · Frontiers in Physics · doi:10.3389/fphy.2022.839462 · Cited by 3
“The flourishing development of e-commerce is making broad and far-reaching impacts on our daily lives, leading consumers to increasingly rely on the Internet to obtain information about products and services to decide how to consume [1, 2, 3].”
Background
Characterizing the reputation of evaluators using vectors in the object feature space
Li, M., Jiang, Y., & Di, Z. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117136 · Cited by 3
If you move, I move: The social influence effect on residential mobility
de la Prada, À. G., & Tapia, E. (2022) · PLoS ONE · doi:10.1371/journal.pone.0270783 · Cited by 1
A Warning Approach to Mitigating Bandwagon Bias in Online Ratings: Theoretical Analysis and Experimental Investigations
Wu, D. J., Guo, X., Wang, Y., & Chen, G. (2023) · Journal of the Association for Information Systems · doi:10.17705/1jais.00817 · Cited by 3
Linking negative herding effect to online public opinion: A new BiGADG approach for sentiment classification
Zhou, Y., Hu, Y., Liu, H., Huang, P., & Dai, D. (2024) · Expert Systems · doi:10.1111/exsy.13587 · Cited by 3
“Muchnik et al. (2013) discovered that positive social influences in electoral voting cumulatively produce a rating bubble tendency, while negative social influences are neutralized by crowd correction.”
Background
Parent-subsidiary dispersion and ESG rating disparities: evidence from China
Li, B., Xu, X., Qin, B., & Yang, X. (2025) · Accounting and Business Research · doi:10.1080/00014788.2025.2518082 · Cited by 3
Noise or Quality? Cross-Nested Hierarchical Effects of Culture on Online Ratings
Mattson, T. (2017) · Communications of the Association for Information Systems · doi:10.17705/1cais.04025 · Cited by 3
Considering Network Effects in the Design and Analysis of Field Experiments on State Legislatures
Phadke, S., & Desmarais, B. A. (2019) · State Politics & Policy Quarterly · doi:10.1177/1532440019859819 · Cited by 3
“A growing body of research seeks to study interference through experimental interventions on networks (e.g., Aral and Walker 2014; Bapna and Umyarov 2015; Bond et al. 2012; Gerber, Green, and Larimer 2008; Muchnik, Aral, and Taylor 2013; Paluck 2011; ben-Aaron, Denny, Desmarais, and Wallach, 2017).”
Background
Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making
Sarkar, S., Shakarian, P., Sanchez, D. N., Armenta, M., & Lakkaraju, K. (2020) · PLoS ONE · doi:10.1371/journal.pone.0234875 · Cited by 4
“This includes revising their initial opinions by processing and averaging the different influences acting on them [33] and social information provides one mechanism through which this is achieved.”
Background
Knowledge Flows and Influence in Online Social Networks: Proposing a Research Agenda
Weber, C. M., & Mayande, N. V. (2017) · Portland International Conference on Management of Engineering and Technology (PICMET) · doi:10.23919/picmet.2017.8125344 · Cited by 3
Songs of a Future Past — An Experimental Study of Online Persuaders
Antenore, M., Panconesi, A., & Terolli, E. (2018) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v12i1.15032 · Cited by 4
“Other web-based experiments in the same vein are (Sal-ganik and Watts 2008), which analysed the impact of social inﬂuence on music, (Muchnik, Aral, and Taylor 2013) exploring news aggregation, (Van de Rijt et al. 2014) for human rewarding systems, (Chen 2008) for online book purchasing , (Zhu and…”
BackgroundResult
Directionality of information flow and echoes without chambers
Kim, S. (2019) · PLoS ONE · doi:10.1371/journal.pone.0215949 · Cited by 3
Identifying and Quantifying Coordinated Manipulation of Upvotes and Downvotes in Naver News Comments
Jeong, J., Kang, J., & Moon, S. B. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7301 · Cited by 3
“Also, recent randomized experiments have shown that a small number of vote manipulation often affect the ﬁnal rating sig-niﬁcantly (Muchnik, Aral, and Taylor 2013; Glenski and Weninger 2017; Carman et al. 2018).”
Background
The Relationship Between Intention to Use, Popularity Information About a Technology, and Trust in Predecessors and Vendors
Vedadi, A., & Greer, T. H. (2021) · Information Resources Management Journal · doi:10.4018/irmj.2021010103 · Cited by 4
Care Quality, Technology Use, and Online Patient Perception
Saifee, D. H., Bardhan, I. R., Lahiri, A., et al. (2019) · Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences · doi:10.24251/hicss.2019.801 · Cited by 3
The Benefits of Product Quality Disagreement - When Divergent Reviews Drive Online Word of MouthPreprint
Nagle, F., & Riedl, C. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2259055 · Cited by 3
“…led to an interest in the dynamics of WOM itself as studies explored what affected the valence of reviews (Moon et al., 2010; Godes and Silva, 2011; Muchnik et al., 2013), the propensity to post a review (Dellarocas and Narayan, 2006; Dellarocas et al., 2010), or both (Moe and Schweidel, 2012).1…”
Product Information Websites: Are They Good for Consumers?Preprint
Markopoulos, P., Aron, R., & Ungar, L. (2014) · SSRN Electronic Journal · doi:10.2139/ssrn.2470624 · Cited by 3
Motivating User-Generated Content with Performance Feedback: Evidence from Randomized Field ExperimentsPreprint
Huang, N., Burtch, G., Gu, B., et al. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2971783 · Cited by 3
When Images Backfire: The Effect of Customer-Generated Images on Product Rating DynamicsPreprint
Guan, Y., Tan, Y., Wei, Q., et al. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3633590 · Cited by 3
Behavioral Biases in MarketingPreprint
Guhl, D., Klapper, D., Massner, K., Spann, M., Stich, L., & Yegoryan, N. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3091557 · Cited by 4
Social Learning in Prosumption: Evidence from a Randomized Field ExperimentPreprint
Jung, J., Sun, T., Bapna, R., & Golden, J. M. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3796254 · Cited by 3
Inclusion, equality and bias in designing online mass deliberative platformsPreprint
Shortall, R., Itten, A. V., van der Meer, M., Murukannaiah, P. K., & Jonker, C. M. (2021) · arXiv (Cornell University) · Cited by 3
Fifty Million Followers Can’t Be Wrong, or Can They? Effects of Social Media Feedback on CEO CommunicationPreprint
Zohrehvand, A. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4141444 · Cited by 3
Why Do Product Prices Affect Review Ratings? Evidence from a Japanese Online Travel AgencyPreprint
Sunada, T. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4814344 · Cited by 3
OASIS: Open Agent Social Interaction Simulations with One Million Agents
Yang, Z., Zhang, Z., Zheng, Z., Jiang, Y., Gan, Z., Wang, Z., Ling, Z., Chen, J., Ma, M., Dong, B., et al. (2024) · arXiv · doi:10.48550/arxiv.2411.11581 · Cited by 3
“Specifically, we simulate information propagation (Vosoughi et al., 2018) and the resulting group polarization (Lindesmith et al., 1999) on rapid information exchange platforms like X and the herd effect (Muchnik et al., 2013) on topic-based community-oriented platforms like Reddit.”
Understanding Cancer Survivorship Care Needs Using Amazon Reviews: Content Analysis, Algorithm Development, and Validation Study
Wang, L., Lu, Q., Li, R., Harrison, T. B., Jia, H., Huang, M. S., Dowst, H., Zhang, R., Badr, H., Fan, J. W., et al. (2025) · JMIR Cancer · doi:10.2196/71102 · Cited by 2
“In addition, consumer reviews are inherently subjective and may reflect social influence biases (eg, other reviews) [45], or may come from users who are not representative of the broader survivor population [46].”
Collective Performance Induced by Social and Individual Learning in Any Population Structure: An Evolutionary Game Approach
Li, Z., Zhang, J., Chen, X., & Szolnoki, A. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3592636 · Cited by 2
Opinion leaders vs collective dynamics in cultural consumption: Decision-making in the choice of music
Kasaras, K., Klimis, G. M., & Michailidou, M. (2025) · Collective Intelligence · doi:10.1177/26339137251387144 · Cited by 2
“…within social networks (Katz and Lazarsfeld, 1955; Watts and Dodds, 2007), the signi ﬁ - cance of cultural intermediaries (Bourdieu, 1984; Maguire and Matthews, 2012), and, more recently, the ef ﬁ cacy of collective social signals in digital contexts (Muchnik et al., 2013; Salganik et al., 2006).”
Hug fans or follow celebrities? How nationalism is reinforced on Chinese social media
Hong, J. Y., Kim, Y. H., Zhang, H., & Qin, T. (2025) · Science Advances · doi:10.1126/sciadv.adu8241 · Cited by 2
Past stays and future standards: How customer-mediated knowledge transfer enhances service quality
Zhang, X., Huang, X., Yang, Y., & Liu, W. (2025) · Tourism Management · doi:10.1016/j.tourman.2025.105341 · Cited by 3
Unveiling consumer preferences: a theory-based multimodal deep learning architecture for explainable recommendation
Liu, X., Yang, Q., Liu, Y., Shang, J. S., Jiang, Y., & Chai, Y. (2026) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2026.01.007 · Cited by 3
Dominance or Fair Play in Social Networks? A Model of Influencer Popularity Dynamics
Galante, F., Ravazzi, C., Vassio, L., Garetto, M., & Leonardi, E. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-13821-7_26 · Cited by 2
From Likes to Algorithms: A Systematic Review of Online Social Conformity Determinants and Consequences
Batool, H., & Batool, I. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2631667 · Cited by 2
Attractability and virality: The role of message features and social influence in health news diffusion
Kim, H. S. (2014) · Scholarly Commons (University of Pennsylvania) · Cited by 2
Estimating the Effect of Social Influence on Subsequent Reviews
Han, S., & Anderson, C. K. (2018) · Springer proceedings in business and economics · doi:10.1007/978-3-030-04726-9_23 · Cited by 2
Novel idea generation in social networks is optimized by exposure to a “Goldilocks” level of idea-variability
Baten, R. A., Aslin, R. Ν., Ghoshal, G., et al. (2022) · PNAS Nexus · doi:10.1093/pnasnexus/pgac255 · Cited by 2
“People can conform to social influence when they assume others to be better informed than they are or believe that the majority’s pick is correct (2,16,19,20).”
Designing for Constructive Online Discussion
Lieu, E. (2022) · Electronic workshops in computing · doi:10.14236/ewic/hci2022.61 · Cited by 2
Enhanced Customer Lifetime Value Modeling: Integrating Conformity and Network Influence Through Sentiment Analysis
Nazerian, N., & Amiri, B. (2024) · IEEE Access · doi:10.1109/access.2024.3430503 · Cited by 2
Contextual Bandit with Herding Effects: Algorithms and Recommendation Applications
Xu, L., Wang, L., Xie, H. L., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0128-8_12 · Cited by 2
Detecting Bias in Large Language Models: Fine-Tuned KcBERT
Lee, J., & Chung, T. (2025) · Lecture notes in computer science · doi:10.1007/978-981-97-8705-0_6 · Cited by 5
Collective Superintelligence: Enabling Real-Time Conversational Deliberations among Humans and AI Agents at Unprecedented Scale
Rosenberg, L. B. (2025) · IntechOpen eBooks · doi:10.5772/intechopen.1010201 · Cited by 3
Analyzing Tourist Online Environmental Discourse With Big Data: A Study of 43 UNESCO Natural World Heritage Sites
Yousaf, S., & Kim, J. M. (2025) · International Journal of Tourism Research · doi:10.1002/jtr.70087 · Cited by 2
Design of Contextual Local Expert Support Mechanism
Liou, J.-H., & Li, Y.-M. (2015) · International Conference on Electronic Commerce · doi:10.1145/2781562.2781581 · Cited by 2
WHY AMAZON'S RATINGS MIGHT MISLEAD YOU
Wang, T., & Wang, D. (2014) · Cited by 2

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
ORCID: Muchnik, Lev
Aral, Sinan
Taylor, Sean J.
Venue: Science
Publisher: American Association for the Advancement of Science (AAAS)
Published: 2013-08-09
Volume: 341
Issue: 6146
Pages: 647-651
ISSN: 0036-8075 (ISSN-L)
1095-9203
DOI: 10.1126/science.1240466
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.

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

Social Influence Bias: A Randomized Experiment

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks