The DOI matches its Crossref record; 20 of the 21 references it cites were found in a registry or an index.
Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market
Abstract
Hit songs, books, and movies are many times more successful than average, suggesting that “the best” alternatives are qualitatively different from “the rest”; yet experts routinely fail to predict which products will succeed. We investigated this paradox experimentally, by creating an artificial “music market” in which 14,341 participants downloaded previously unknown songs either with or without knowledge of previous participants' choices. Increasing the strength of social influence increased both inequality and unpredictability of success. Success was also only partly determined by quality: The best songs rarely did poorly, and the worst rarely did well, but any other result was possible.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“Rosen's economics of superstars [21] shows formally that when the cost of serving additional consumers approaches zero, cultural markets concentrate rewards on a small number of winners regardless of marginal quality differences”
From AI as a Democratizing Force in Indie Game Development (2026) · cites this paper as [21]
In the citing paper
“The market-structure literature of §2.4 predicts what follows: superstar concentration under near-zero marginal cost [21], against which the long-tail alternative [22] is directly testable with the outcome data of Table 13, and choice-abundant markets whose winners are selected by social dynamics rather than quality gradients [23].”
From AI as a Democratizing Force in Indie Game Development (2026) · cites this paper as [21]
In the citing paper
“When considering inequality in heterogeneous populations, there are two distinct aspects to take into account-the dispersion of outcomes and the predictability of outcomes (Lynn, Podolny, & Tao, 2009;Salganik, Dodds, & Watts, 2006).”
From Relative Feedback Increases Disparities in Effort and Performance in Crowdsourcing Contests: Evidence from a Quasi-Experiment on Topcoder (2022) · cites this paper as Salganik, Dodds, & Watts, 2006
In the citing paper
“In the case of an inherent quality or virtue, more predictable outcomes imply more equality and fairness (Cook & Hegtvedt, 1983;Jasso, 1983;Salganik et al., 2006).”
From Relative Feedback Increases Disparities in Effort and Performance in Crowdsourcing Contests: Evidence from a Quasi-Experiment on Topcoder (2022) · cites this paper as Salganik et al., 2006
In the citing paper
“However, social success and quality of content are only partly correlated (Salganik et al., 2006).”
From Social Drivers and Algorithmic Mechanisms on Digital Media. (2024) · cites this paper as Salganik et al., 2006
In the citing paper
“in a classic experiment, the popularity of all but the very best and worst songs was shown to be more related to stochastic early positive reception by other users than by their inherent quality”
From Stewardship of global collective behavior (2021) · cites this paper as [87]
In the citing paper
“Moreover, the presence of a significant number of neutral individuals holding a random opinion and a low confidence level around these two attractive forces tends to increase the unpredictability of the final outcome [15].”
From Social Influence and the Collective Dynamics of Opinion Formation (2013) · cites this paper as [15]
In the citing paper
“In agreement with previous works [15], the collective outcome appears to be poorly predictable and strongly dependent on the initial conditions [36].”
From Social Influence and the Collective Dynamics of Opinion Formation (2013) · cites this paper as [15]
In the citing paper
“Although there is on average a positive relationship between quality and success [66]”
From Relative Citation Ratio (RCR): A New Metric That Uses Citation Rates to Measure Influence at the Article Level (2016) · cites this paper as [66]
In the citing paper
“the ultimate winners in such contests are determined not only by the intrinsic value of the work but also by more intangible social variables [66].”
From Relative Citation Ratio (RCR): A New Metric That Uses Citation Rates to Measure Influence at the Article Level (2016) · cites this paper as [66]
Citations
Cites
Riggins, F. J., & Mitra, S. (1990) · Choice Reviews Online · doi:10.5860/choice.27-6637 · Cited by 16,590
Social Influence: Compliance and Conformity
Cialdini, R. B., & Goldstein, N. J. (2004) · Annual Review of Psychology · doi:10.1146/annurev.psych.55.090902.142015 · Cited by 6,077
A simple model of global cascades on random networks
Watts, D. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.082090499 · Cited by 2,673
Self-citationA Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades
Bikhchandani, S., Hirshleifer, D. A., & Welch, I. (1992) · Journal of Political Economy · doi:10.1086/261849 · Cited by 1,980
(1998) · Cambridge University Press eBooks · doi:10.1017/cbo9780511663901 · Cited by 1,774
(2009) · Harvard University Press eBooks · doi:10.2307/j.ctv1kz4h99.12 · Cited by 1,685
Culture and conformity: A meta-analysis of studies using Asch's (1952b, 1956) line judgment task.
Bond, R. A., & Smith, P. B. (1996) · Psychological Bulletin · doi:10.1037/0033-2909.119.1.111 · Cited by 1,466
Processing Fads and Fashions: An Organization-Set Analysis of Cultural Industry Systems
Hirsch, P. M. (1972) · American Journal of Sociology · doi:10.1086/225192 · Cited by 1,331
Walker, D. M., Frank, R. H., & Cook, P. J. (1996) · Southern Economic Journal · doi:10.2307/1061199 · Cited by 1,180
Allison, P. D. (1978) · American Sociological Review · doi:10.2307/2094626 · Cited by 1,133
Research works printed but not linked: 4
- R. E. Caves Creative Industries (Harvard Univ. Press Cambridge MA 2000).
- S. Rosen, Am. Econ. Rev.71, 845 (1981).
- M. Adler, Am. Econ. Rev.75, 208 (1985).
- L. R. Anderson, C. A. Holt, Am. Econ. Rev.87, 847 (1997).
Cited by
Sorted by citations
Items 1,701–1,800 of 2,217
Long Tails and Localized Increasing ReturnsPreprint
Lamberson, P. J. (2008) · SSRN Electronic Journal · doi:10.2139/ssrn.1273200 · Cited by 1
“…in Figure 2, resembles a power law.6 Besides replicating the empirical observations of Kohli and Sah (2006) and Anderson (2006), these results also closely resemble and may serve to explain the experimental results on consumer behavior with social influence obtained by Salganik et al. (2006).”
InfluentialThe Benefits of Segmentation in Trial-Offer Markets with Social Influence and Position Bias.Preprint
Berbeglia, F., Berbeglia, G., & Hentenryck, P. V. (2015) · arXiv (Cornell University) · Cited by 1
Networks and Cultural Consumption
Pachucki, M. C. (2015) · Elsevier eBooks · doi:10.1016/b978-0-08-097086-8.10446-5 · Cited by 1
“In an innovative examination of how network social in fl uence shapes musical tastes, Salganik et al. (2006) developed an online experiment in which participants downloaded and rated music while in some cases aware of others ’ online expressed preferences.”
When and Where Can Institutionalization Occur? The Case of Price Bubbles in Financial MarketsPreprint
Levine, S. S., & Zajac, E. J. (2009) · SSRN Electronic Journal · doi:10.2139/ssrn.1352112 · Cited by 1
Understanding and Overcoming Biases in Customer ReviewsPreprint
Askalidis, G., & Malthouse, E. C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.00417 · Cited by 4
“Salganik et al. (2006) created an artificial music market where participants downloaded previously unknown songs, either with or without information about the previous participants’ actions, and found that the display of social signals increases the inequality and unpredictability of success.”
BackgroundAn information-processing model of bandwagon effects on media users�?? movie viewing selections
Xu, X. (2015) · doi:10.32657/10356/65398 · Cited by 1
“This is consistent with the existing literature of bandwagon effects (e.g., De Vany & Walls, 1996; Fu & Sim, 2011; Knobloch-Westerwick et al., 2005; Salganik et al., 2006; Xu & Fu, 2013).”
ResultBeyond Exchangeability: The Chinese Voting ProcessPreprint
Lee, M., Jin, S. H., & Mimno, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.09428 · Cited by 1
“Previous work in self-reinforcing social behaviors shows that although inherent quality is an important factor in overall ranking, users are susceptible to position bias [12, 13].”
Excess Reciprocity Distorts Reputation in Online Social NetworksPreprint
Livan, G., Caccioli, F., & Aste, T. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3112117 · Cited by 2
Discriminative Modeling of Social Influence for Prediction and Explanation in Event Cascades.Preprint
Soni, S., Ramirez, S. L., & Eisenstein, J. (2018) · arXiv (Cornell University) · Cited by 1
Using Latent Variable Models to Improve Causal Estimation
Oktay, H. (2021) · Scholarworks (University of Massachusetts Amherst) · doi:10.7275/11261040.0 · Cited by 1
Cultural Production and Circulation
Lena, J. C., & Schmutz, V. (2017) · doi:10.1093/obo/9780199756384-0195 · Cited by 1
Digitization and Distribution: The Ends Against the MiddlePreprint
Yang, J., Anderson, E. T., & Gordon, B. R. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3185847 · Cited by 1
Modeling and Analysis of Tagging Networks in Stack Exchange CommunitiesPreprint
Fu, X., Yu, S., & Benson, A. R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.02372 · Cited by 1
Social Media as Media Literacy
Kimbrough, A. M., & Guadagno, R. E. (2019) · The International Encyclopedia of Media Literacy · doi:10.1002/9781118978238.ieml0224 · Cited by 1
Credibility and Dynamics of Collective AttentionPreprint
Mitra, T., Wright, G. W., & Gilbert, É. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.08440 · Cited by 1
Selection, Consumption, and New Music Exploration in an Online Social Network: A Dyadic ApproachPreprint
Loh, J. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3350524 · Cited by 1
“Those studies do not paint a clear picture, with some finding an increase in concentration (Cai et al. 2009, Salganik et al. 2006, Sorensen 2007), while others find the opposite (Oestreicher-Singer & Sundararajan 2012 a , b ), suggesting heterogeneity in the effect.”
BackgroundBig Data for Sociological Research
Radford, J. P., & Lazer, D. M. J. (2019) · Wiley eBooks · doi:10.1002/9781119429333.ch24 · Cited by 2
Zhang, B., Geng, R., Chen, X., & Pavlou, P. A. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3237058 · Cited by 2
Huang, N., He, Y., Xu, X., & Hong, Y. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3514033 · Cited by 1
Exploiting Review Neighbors for Contextualized Helpfulness PredictionPreprint
Du, J., Rong, J., Wang, H., & Zhang, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.09685 · Cited by 1
“Social Influence on Helpfulness Perception Social influence [12, 59] has been proven to be a key part in decisionmaking through extensive experiments [73, 61, 5, 28, 55] in psychology, economics, sociology, and human behavior analysis.”
BackgroundAdvanced Review Helpfulness Modeling
Du, J. (2020) · Victoria University Research Repository (Victoria University) · Cited by 1
Models and Anti-Models: The Structure of Payoff-Dependent Social LearningPreprint
Efferson, C. M., Lalive, R., Richerson, P. J., McElreath, R. L., & Lubell, M. N. (2006) · SSRN Electronic Journal · doi:10.2139/ssrn.905250 · Cited by 1
“…multiple equilibria (Anderson and Holt, 1997; Banerjee, 1992; Baum et al., 2004; Bikhchandani et al., 1992; Boyd and Richerson, 1992; Bowles, 2004; Salganik et al., 2006), extreme behaviors (Boyd and Richerson, 1985), cultural group selection (Henrich, 2004), ethnically marked social groups (Boyd…”
BackgroundMicroeconomics and PsychologyPreprint
DeAngelo, G. J., Beckman, S. R., Chen, L., et al. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1893788 · Cited by 1
Socially Embedded Knowledge Network and the Making of Opinion Leaders: Evidence from TwitterPreprint
Bo, H. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3493065 · Cited by 1
Zhang, J. (2012) · UA Campus Repository (The University of Arizona) · Cited by 1
Shore, J. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2220443 · Cited by 1
Audience Engagement and the Dynamics of Online Activism: Far-Right Mobilization on FacebookPreprint
Davidson, T. (2021) · doi:10.31235/osf.io/rwfjc · Cited by 1
A proposal to improve research in AI algorithm and data governancePreprint
Braga, J., Regateiro, F. S., Stiubiener, I., & Braga, J. C. (2022) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/sr7kt · Cited by 1
Untapped Potential: Designed Digital Trace Data in Online Survey ExperimentsPreprint
Macke, E., Daviss, C., & Williams-Baron, E. (2022) · doi:10.31235/osf.io/frhj6 · Cited by 1
Predictability Hypotheses: A Meta-Theoretical and Methodological IntroductionPreprint
van Loon, A. (2022) · doi:10.31235/osf.io/s7uer · Cited by 1
The Other Invisible Hand: How Markets - As Institutions - Propagate Conformity and Valuation ErrorsPreprint
Levine, S., & Zajac, E. (2023) · Social Science Research Network · doi:10.2139/ssrn.3536023 · Cited by 4
Cyber Income InequalityPreprint
Liao, L., Chen, L., Liu, L., Wang, X., & You, Y. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4381802 · Cited by 1
An American Bias in a Local Music Streaming Market?Preprint
Moreau, F., Lumeau, M., Wikström, P., Haampland, O., & Johannessen, R. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4499080 · Cited by 1
Estimating Position and Social Influence Effects in Online SearchPreprint
Osgouei, A. J., Ching, A. T., Ratchford, B. T., et al. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4545610 · Cited by 1
Group problem solving: Harnessing the Wisdom of the CrowdsPreprint
Hills, T. T. (2023) · doi:10.31234/osf.io/eyrzg · Cited by 1
Scaling up experimental social, behavioral, and economic sciencePreprint
Almaatouq, A., Becker, J. A., Bernstein, M. S., Botto, R., Bradlow, E. T., Damer, E., Duckworth, A. L., Griffiths, T. L., Hartshorne, J. K., Law, E., et al. (2021) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/wksv8_v1 · Cited by 1
Self-citationCreative Production in the UK Music Industries
Cluley, R. (2011) · OpenGrey (Institut de l'Information Scientifique et Technique) · Cited by 1
Understanding Compliance and Conversion Dynamics in Multi-Agent Collectives
Lee, S., & Lee, K. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790385 · Cited by 2
“Studies reveal how early ratings bias subsequent choices [47, 61], exposure to others’ preferences shifts individual selections over time [80], and interactive features with visible responses amplify conformity effects [74].”
Intellectual Property Incentives: Economics and Policy Implications *Preprint
Maurer, S. M. (2016) · SSRN Electronic Journal · Cited by 1
How Partisan Crowds Affect News Evaluation
Jakesch, M., Koren, M., Evtushenko, A., & Naaman, M. (2022) · doi:10.31234/osf.io/baxts
Comprendre les transformations de l’industrie musicale
Ruiz, É., Tellier, A., & Pénin, J. (2021) · Revue française de gestion · doi:10.3166/rfg.2021.00505 · Cited by 2
Shifting behaviour of users: Towards understanding the fundamental law of Social Networks
Gupta, Y., Saini, J. S., Sridhar, N., & Iyengar, S. R. S. (2016) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets.2016.7439982
“Watts conducted an experiment [12] to prove the increase in the unpredictability of the success of a product in an environment with increasing social influence.”
BackgroundA field study of social learning
Fishman, A., & Gneezy, U. (2011) · Econstor (Econstor)
Estrada, F., & Salazar, B. (2010) · SSRN Electronic Journal · doi:10.2139/ssrn.1691380
Private and public discrepancy : the anatomy of valuation in market
Zhang, J. (2014) · DSpace@MIT (Massachusetts Institute of Technology)
Using Visibility to Control Collective Attention in Crowdsourcing
Lerman, K., & Hogg, T. (2013) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v1i1.13098
“After enough people examine an item, its quality, or how interesting it is to people, should be reflected in the number of recommendations it receives (its popularity) (Salganik, Dodds, and Watts 2006).”
BackgroundVirtual Worlds as Laboratories
Ross, T. L., Castronova, E., & Knowles, I. (2015) · Emerging Trends in the Social and Behavioral Sciences · doi:10.1002/9781118900772.etrds0378 · Cited by 1
The diffusion of innovations in the presence of geography and media
Toole, J. L. (2012) · DSpace@MIT (Massachusetts Institute of Technology)
Estrada, F., & Salazar, B. (2011) · Análisis Político
Others bought it too: the relevance of knowing purchase levels
Ali, H., Parsons, A., & Ballantine, P. W. (2013) · Tuwhera (Auckland University of Technology)
Learning from collective preferences, behavior, and beliefs
Kearns, M. J., & Vaughan, J. W. (2009)
Trusted Computing in mobiler Anwendung: Von Zugangskontrolle zu Identitäten
Schmidt, A., & Kuntze, N. (2008) · Vieweg+Teubner eBooks · doi:10.1007/978-3-8348-9452-6_14
Social Experiments and Computing
Helbing, D. (2012) · Understanding complex systems · doi:10.1007/978-3-642-24004-1_11
Un mundo sin elefantes y un México competitivo
Castañeda, G. (2012) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)
An Explanation to Individual Knowledge and Behavior Based on Empirical Substrates
Zhao, L., & Zhu, X. C. (2008) · Munich Personal RePEc Archive (Ludwig Maximilian University of Munich)
The Effect of Biased Communications On Both Trusting and Suspicious VotersPreprint
Cohen, W. W., Redlawsk, D. P., & Pierce, D. R. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1306.2558
Spitzer, M. (2006) · Nervenheilkunde · doi:10.1055/s-0038-1626745
“Genau hier setzt eine Studie an, deren Ziel es war, „Kulturgeschichte“ experimentell zu untersuchen (8).”
BackgroundOvercoming Bias in the Deliberations of Distributed Teams
Resnick, M. L. (2009) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/154193120905300602
Popularity as a Factor in Copyright Law
Shur‐Ofry, M. (2009) · University of Toronto Law Journal · doi:10.1353/tlj.0.0032
Prémices d’une industrialisation de la communication du téléacteur sénégalais et tunisien
Tine, B., & Elhaou, M. A. (2013) · Communication · doi:10.4000/communication.4717
Exploring Learning Pattern in Social Network
Banerjee, S., & Caballé, S. (2011) · International Conference on Intelligent Networking and Collaborative Systems (INCoS) · doi:10.1109/incos.2011.90
“Section 3 elaborates specific collective behaviour and learning experienced in different instances like herding behaviours in financial markets [4], Hollywood trends [6], and critical mass phenomena in group activities [5], which are fare amounts of automated blends in learning and decision making through natural agen…”
BackgroundLovely weather, isn’t it? On the social dynamics of quality judgment
Nowak, A. K., Samson, K., Lisiecka, K., et al. (2011) · Mind & Society · doi:10.1007/s11299-011-0088-3
“Recently, in an experimental study on preference formation in a virtual musical market, Salganik et al. (2006) showed that once the social factor comes into play, it is almost impossible to predict people’s choices based on the objective quality of rated songs.”
BackgroundIranmanesh, M., Jayaraman, K., & Ismail, I. (2014) · Verslas teorija ir praktika · doi:10.3846/btp.2014.435
Information and social system interaction
Friedman, E., & Haque, A. (2011)
Ormerod, P. (2012) · Security Informatics · doi:10.1186/2190-8532-1-16
“Complex choices can be fundamentally different from simple two-choice scenarios, such that the problem becomes very difficult to predict, as has been demonstrated in ecological [7] and human settings [8].”
BackgroundZhao, L. (2008) · Munich Personal RePEc Archive (Ludwig Maximilian University of Munich)
An Economic Approach to Consumer Product Ratings
Beckett, D. (2013) · doi:10.7907/00ee-kp91
Brains that Make RevolutionsPreprint
Estrada, F., & Salazar, B. (2010) · SSRN Electronic Journal · doi:10.2139/ssrn.1692167
Network Characteristics and Dynamics: Reciprocity, Competition and Information Dissemination
Jiang, B. (2021) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · doi:10.7275/7295597.0
Popularity and Performance: A Large-Scale StudyPreprint
Krafft, P. M., Zheng, J., Shmueli, E., et al. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.7729
“Recently researchers have begun using large-scale online experiments to study the effect of cumulative advantage in realistic scenarios [Salganik et al. 2006], [Muchnik et al.”
BackgroundFu, W., & Pirolli, P. L. T. (2013) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199757183.013.0035
“An example that demonstrates the importance of establishing this micro-to-macro link is the study by Salganik et al. (2006).”
BackgroundVirala nyheter: Hur nyheter sprids och bemöts i sociala medier
Dahlgren, P. (2014) · Gothenburg University Publications Electronic Archive (Gothenburg University)
Book Review: Damon J. Phillips Shaping Jazz: Cities, Labels, and the Global Emergence of an Art Form
Godart, F. C. (2014) · Organization Studies · doi:10.1177/0170840614526680
An equilibrium approach to modelling social interaction
Parmeggiani, A., & Contucci, P. (2009)
The influence of awards on the demand for recorded music
Jurkovicova, Z. (2017)
Principles of Complex Systems Course 300, Fall, 2008
Dodds, P. S. (2008)
Self-citationRatings in Social Music Cataloging Services
Nurminen, M. (2008)
The Dynamics of Social Inuence
Pradelski, B. (2015)
Schwagereit, F., Gottron, T., & Staab, S. (2014)
An Explanation to Individual Knowledge and Behavior Based on Empirical
Zhao, L., & Zhu, X. C. (2008)
Principles of Complex Systems Course CSYS/MATH 300, Fall, 2009
Dodds, P. S. (2009)
Self-citationA survey on predicting the popularity of web
de Amorim, M. D., & Antoniadis, P. (2014)
Dodds, P. S. (2013)
Self-citationQuantifying the eects of social influencePreprint
Mavrodiev, P., Tessone, C. J., & Schweitzer, F. (2013) · arXiv (Cornell University)
Celma, Ò. (2010) · Springer eBooks · doi:10.1007/978-3-642-13287-2_6
Culture, Production of: Prospects for the Twenty-First Century
Lena, J. C. (2015) · Elsevier eBooks · doi:10.1016/b978-0-08-097086-8.10417-9
Price Discounts and Peer Effects in Information Goods: Results from a Randomized ExperimentPreprint
de Matos, M. G., Ferreira, P., & Belo, R. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2551800
Measuring and Optimizing Cultural MarketsPreprint
Abeliuk, A., Berbeglia, G., Cebrián, M., & Hentenryck, P. V. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.1542 · Cited by 2
Social Learning in Search Good MarketsPreprint
Newberry, P. W. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2327815
RATIONAL RECONSTRUCTIONS OF SOCIETY
Arnout, V. D. R. (2007) · eCommons (Cornell University)
Stochastic Modeling of Social Behavior on Digg
Hogg, T. (2013) · Springer eBooks · doi:10.1007/978-1-4614-8806-4_62
Sprague, D. A. (2026) · Warwick Research Archive Portal (University of Warwick) · doi:10.82444/warw.33712834
Aspect-based Sentiment Analysis for Social Recommender Systems
Chen, Y. Y. (2015) · Open Access Institutional Repository at Robert Gordon University (Robert Gordon University)
SCALABLE RECOMMENDATION WITH SOCIAL CONTEXTUAL INFORMATION
Fabian, A. (2015) · International Journal of Research in Engineering and Technology · doi:10.15623/ijret.2015.0404028
Toward a synthetic understanding of collective decision-making by humans and non-human animals
Toyokawa, W., & Kameda, T. (2013) · Japanese Journal of Animal Psychology · doi:10.2502/janip.63.2.1
Britton, M. L. (2014)
Metadata
- Authors
- Salganik, M. J., Dodds, P. S., & Watts, D. J.
- Affiliations
- Santa Fe Institute (US)
- Corresponding author
- Salganik, Matthew J.
- Dodds, Peter Sheridan
- Watts, Duncan J.
- Venue
- Science
- Published
- 2006-02-10
- Volume
- 311
- Issue
- 5762
- Pages
- 854-856
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2147453867
- Cited by
- 2,231
- References
- 21
- Citations by year
- 2026: 168 · 2025: 104 · 2024: 102 · 2023: 123 · 2022: 103 · 2021: 231 · 2020: 104 · 2019: 130 · 2018: 131 · 2017: 142 · 2016: 121 · 2015: 123 · 2014: 133 · 2013: 92 · 2012: 122
- FWCI
- 116.70
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Experimental Behavioral Economics Studies
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- cultural market, social influence, music download, product success, consumer choice
- MeSH terms
- Consumer Behavior, Culture, Forecasting, Humans, Interpersonal Relations, Music, Research Design, Social Behavior, Sociology, Internet
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
