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.
Integrity signals
Registry
Cites
Statistics
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?
Rosen's economics of superstars shows that when cost of serving additional consumers approaches zero, cultural markets concentrate rewards on a small number of winners regardless of marginal quality differences.
“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]
The market-structure literature predicts superstar concentration under near-zero marginal cost, against which the long-tail alternative 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.
“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]
Inequality in heterogeneous populations has two distinct aspects: dispersion of outcomes and predictability of outcomes.
“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 (2022) · cites this paper as Salganik, Dodds, & Watts, 2006
In the case of an inherent quality or virtue, more predictable outcomes imply more equality and fairness.
“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 (2022) · cites this paper as Salganik et al., 2006
Social success and quality of content are only partly correlated.
“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 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.
“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]
The presence of a significant number of neutral individuals holding random opinions and low confidence around the attractive forces tends to increase the unpredictability of the final outcome.
“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]
The collective outcome appears poorly predictable and strongly dependent on initial conditions.
“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]
There is on average a positive relationship between quality and success.
“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]
The ultimate winners in competitive market contests are determined not only by the intrinsic value of the work but also by more intangible social variables.
“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]
Cites
Riggins, F. J., & Mitra, S. (1990) · Choice Reviews Online · doi:10.5860/choice.27-6637 · Cited by 16,590 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
(1998) · Cambridge University Press eBooks · doi:10.1017/cbo9780511663901 · Cited by 1,774 · see citing works
(2009) · Harvard University Press eBooks · doi:10.2307/j.ctv1kz4h99.12 · Cited by 1,685 · see citing works
Culture and conformity: A meta-analysis of studies using Asch's (1952b, 1956) line judgment task.
Bond, R., & Smith, P. B. (1996) · Psychological Bulletin · doi:10.1037/0033-2909.119.1.111 · Cited by 1,486 · see citing works
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 · see citing works
Walker, D. M., Frank, R. H., & Cook, P. J. (1996) · Southern Economic Journal · doi:10.2307/1061199 · Cited by 1,180 · see citing works
Allison, P. D. (1978) · American Sociological Review · doi:10.2307/2094626 · Cited by 1,133 · see citing works
Adler, M. (2016) · American Economic Review · Cited by 391 · see citing works
Entrepreneurship in Organizations: Evidence from the Popular Music Industry
Peterson, R. A., & Berger, D. G. (1971) · Administrative Science Quarterly · doi:10.2307/2391293 · Cited by 365 · see citing works
Bielby, W. T., & Bielby, D. D. V. (1994) · American Journal of Sociology · doi:10.1086/230412 · Cited by 336 · see citing works
The Economics of Real Superstars: The Market for Rock Concerts in the Material World
Krueger, A. B. (2005) · Journal of Labor Economics · doi:10.1086/425431 · Cited by 336 · see citing works
Limited Depth of Reasoning and Failure of Cascade Formation in the Laboratory
Kübler, D. F., & Weizsäcker, G. (2004) · The Review of Economic Studies · doi:10.1111/0034-6527.00290 · Cited by 275 · see citing works
Vany, A. D. (2003) · Routledge eBooks · doi:10.4324/9780203489970 · Cited by 247 · see citing works
Entertainment Industry Economics
Vogel, H. L. (2007) · Cambridge University Press eBooks · doi:10.1017/cbo9780511510786.002 · Cited by 227 · see citing works
Measuring Inequality: A Methodological Handbook.
South, S. J., & Coulter, P. B. (1990) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2072306 · Cited by 220 · see citing works
A Stochastic Model of Superstardom: An Application of the Yule Distribution
Chung, K. H., & Cox, R. A. K. (1994) · The Review of Economics and Statistics · doi:10.2307/2109778 · Cited by 165 · see citing works
稔, 杉. (2002) · W waves · doi:10.4993/wwaves1995.8.22 · Cited by 89 · see citing works
Flagged first, then by citations
1 of the 21 references couldn’t be matched to a known paper.
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
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., et al. (2009) · Science · doi:10.1126/science.1167742 · Cited by 3,365 · see citing works
Conducting behavioral research on Amazon’s Mechanical Turk
Mason, W., & Suri, S. (2012) · Behavior Research Methods · doi:10.3758/s13428-011-0124-6 · Cited by 3,172 · see citing works
“For instance, Snow et al. (2008) showed that for a variety of natural language processing tasks, such as affect recognition and word similarity, combining the output of just a few workers can equal the accuracy of expert labelers.”
The spread of behavior in an online social network experiment
Centola, D. (2010) · Science · doi:10.1126/science.1185231 · Cited by 2,692 · see citing works
“(iv) Finally, this design allows the same diffusion process to be observed multiple times, under identical structural conditions, thus allowing the often stochastic process of individual adoption (21) to be studied in a way that provides robust evidence for the effects of network topology on the dynamics of diffusion.”
A 61-million-person experiment in social influence and political mobilization
Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D. I., Marlow, C., Settle, J. E., & Fowler, J. H. (2012) · Nature · doi:10.1038/nature11421 · Cited by 2,575 · see citing works
Newman, M. E. J. (2018) · doi:10.1093/oso/9780198805090.001.0001 · Cited by 2,317 · see citing works
DiPrete, T. A., & Eirich, G. M. (2006) · Annual Review of Sociology · doi:10.1146/annurev.soc.32.061604.123127 · Cited by 2,069 · see citing works
Influentials, Networks, and Public Opinion Formation
Watts, D. J., & Dodds, P. S. (2007) · Journal of Consumer Research · doi:10.1086/518527 · Cited by 1,864 · see citing works
“Thus, whatever influence these individuals exert on the collective outcome is an accidental consequence of their randomly assigned position in the queue—a result that has been demonstrated recently for more complicated and realistic scenarios as well (Salganik et al. 2006).”
BackgroundSelf-citationToward a Comparative Sociology of Valuation and Evaluation
Lamont, M. (2012) · Annual Review of Sociology · doi:10.1146/annurev-soc-070308-120022 · Cited by 1,492 · see citing works
How simple rules determine pedestrian behavior and crowd disasters
Moussaïd, M., Helbing, D., & Theraulaz, G. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1016507108 · Cited by 1,204 · see citing works
“We also expect that our heuristics-based approach will inspire new models of collective human behavior such as group decision making (34) and certain social activity patterns (35, 36), where the occurrence of simultaneous interactions between multiple individuals matters.”
The spread of low-credibility content by social bots
Shao, C., Ciampaglia, G. L., Varol, O., Yang, K.-C., Flammini, A., & Menczer, F. (2018) · Nature Communications · doi:10.1038/s41467-018-06930-7 · Cited by 1,125 · see citing works
How social influence can undermine the wisdom of crowd effect
Lorenz, J., Rauhut, H., Schweitzer, F., & Helbing, D. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1008636108 · Cited by 1,017 · see citing works
Internal Social Capital and the Attraction of Early Contributions in Crowdfunding
Colombo, M. G., Franzoni, C., & Rossi‐Lamastra, C. (2014) · Entrepreneurship Theory and Practice · doi:10.1111/etap.12118 · Cited by 1,025 · see citing works
Lü, L., Medo, M., Yeung, C. H., Zhang, Y., Zhang, Z., & Zhou, T. (2012) · Physics Reports · doi:10.1016/j.physrep.2012.02.006 · Cited by 997 · see citing works
“Social filtering Recommendations made by a recommender system are often less appreciated than those coming from our friends [210] and social influences may play a more important role than similarity of past activities [211, 212].”
BackgroundMessing, S., & Westwood, S. J. (2014) · Communication Research · doi:10.1177/0093650212466406 · Cited by 902 · see citing works
Rational Herding in Microloan Markets
Zhang, J., & Liu, P. (2012) · Management Sciences · doi:10.1287/mnsc.1110.1459 · Cited by 904 · see citing works
“Through field experiments , Salganik et al. (2006) find that music consumers seek frequently downloaded songs; Cai et al. (2009) find that restaurant customers prefer popular dishes; Tucker and Zhang (2011) find that displaying click count information attracts Web visitors to popular vendors,…”
BackgroundSocial Influence Bias: A Randomized Experiment
Muchnik, L., Aral, S., & Taylor, S. J. (2013) · Science · doi:10.1126/science.1240466 · Cited by 871 · see citing works
Burtch, G., Ghose, A., & Wattal, S. (2013) · Information Systems Research · doi:10.1287/isre.1120.0468 · Cited by 847 · see citing works
“Lastly, Salganik et al. (2006), manipulating download statistics for songs in an online experiment using an artificial marketplace, find that variation in the presence and ordering of prior download volumes directly influences the inequality and predictability of song success.”
Information contagion: an empirical study of the spread of news on digg and twitter social networks
Lerman, K., & Ghosh, R. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14021 · Cited by 813 · see citing works
Why Copy Others? Insights from the Social Learning Strategies Tournament
Rendell, L., Boyd, R., Cownden, D., Enquist, M., Eriksson, K., Feldman, M. W., Fogarty, L., Ghirlanda, S., Lillicrap, T., & Laland, K. N. (2010) · Science · doi:10.1126/science.1184719 · Cited by 811 · see citing works
Cheng, J., Adamic, L. A., Dow, P. A., Kleinberg, J. M., & Leskovec, J. (2014) · Cited by 771 · see citing works
Uniting the Tribes: Using Text for Marketing Insight
Berger, J., Humphreys, A., Ludwig, S., Moe, W. W., Netzer, O., & Schweidel, D. A. (2020) · Journal of Marketing · doi:10.1177/0022242919873106 · Cited by 742 · see citing works
Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity
Fleder, D. M., & Hosanagar, K. (2009) · Management Science · doi:10.1287/mnsc.1080.0974 · Cited by 749 · see citing works
“lar results were confirmed experimentally by Salganik et al. (2006).”
Online Social Interactions: A Natural Experiment on Word of Mouth versus Observational Learning
Chen, Y., Wang, Q., & Xie, J. (2011) · Journal of Marketing Research · doi:10.1509/jmkr.48.2.238 · Cited by 716 · see citing works
“…recent field studies have collected data to directly measure the effect of WOM or OL, but not both (e.g., Chevalier and Mayzlin 2006; Dholakia and Soltysinkski 2001; Godes and Mayzlin 2004a, b; Hanson and Putler 1996; Liu 2006; Salganik, Dodds, and Watts 2006; Tucker and Zhang 2009; Zhang 2010).”
The “majority illusion” in social networks
Lerman, K., Yan, X., & Wu, X.-Z. (2016) · PLOS ONE · doi:10.1371/journal.pone.0147617 · Cited by 669 · see citing works
Social Networks and the Diffusion of User-Generated Content: Evidence from YouTube
Susarla, A., Oh, J., & Tan, Y. (2011) · Information Systems Research · doi:10.1287/isre.1100.0339 · Cited by 610 · see citing works
“The inequality and unpredictability of success in cultural markets has been attributed to social contagion (Salganik et al. 2006).”
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 · see citing works
“…al., 2012), while social networks, including online social networks, are known to heavily influence opinions and behaviour in many other areas of human activity (Centola, 2010; Christakis and Fowler, 2007, 2008; Fowler and Christakis, 2008; Salganik et al., 2006; Sunstein, 2007; Bond et al., 2012).”
BackgroundMethodInfluentialThe Matthew effect in science funding
Bol, T., de Vaan, M., & van de Rijt, A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1719557115 · Cited by 638 · see citing works
Topic choice contributes to the lower rate of NIH awards to African-American/black scientists
Hoppe, T. A., Litovitz, A. E., Willis, K. A., Meseroll, R. A., Perkins, M. J., Hutchins, B. I., Davis, A. F., Lauer, M. S., Valantine, H. A., Anderson, J. M., et al. (2019) · Science Advances · doi:10.1126/sciadv.aaw7238 · Cited by 627 · see citing works
How Online Product Reviews Affect Retail Sales: A Meta-analysis
Floyd, K., Freling, R. E., Alhoqail, S. A., Cho, H. Y., & Freling, T. H. (2014) · Journal of Retailing · doi:10.1016/j.jretai.2014.04.004 · Cited by 631 · see citing works
Customer-to-Customer Interactions: Broadening the Scope of Word of Mouth Research
Libai, B., Bolton, R. N., Bügel, M. S., de Ruyter, K., Götz, O., Risselada, H., & Stephen, A. T. (2010) · Journal of Service Research · doi:10.1177/1094670510375600 · Cited by 608 · see citing works
“…online environments allow researchers to conduct realistic large-scale experiments that examine how people are affected by others' selections (Salganik, Dodds, and Watts 2006) and they provide new tools, such as ''Google Insights for Search,'' that can help researchers to analyze trends…”
BackgroundThe Dynamics of Audience Fragmentation: Public Attention in an Age of Digital Media
Webster, J. G., & Ksiazek, T. B. (2012) · Journal of Communication · doi:10.1111/j.1460-2466.2011.01616.x · Cited by 605 · see citing works
“Most social media seem to offer some sort of measures that alert users to what their fellow consumers have done or said, often guiding subsequent choices (Cho & Roy, 2004; Salganik, Dodds, & Watts, 2006).”
BackgroundThe Taste for Privacy: An Analysis of College Student Privacy Settings in an Online Social Network
Lewis, K., Kaufman, J. A., & Christakis, N. A. (2008) · Journal of Computer-Mediated Communication · doi:10.1111/j.1083-6101.2008.01432.x · Cited by 597 · see citing works
“Salganik, Dodds, and Watts (2006) found that participants in a ‘‘music market’’ experiment were much more likely to download”
BackgroundThe spread of low-credibility content by social bots
Shao, C., Ciampaglia, G. L., Varol, O., Yang, K.-C., Flammini, A., & Menczer, F. (2018) · RePEc: Research Papers in Economics · Cited by 574 · see citing works
Cheng, J., Adamic, L., Dow, P. A., Kleinberg, J. M., & Leskovec, J. (2014) · International World Wide Web Conference (WWW) · doi:10.1145/2566486.2567997 · Cited by 547 · see citing works
Cultural Holes: Beyond Relationality in Social Networks and Culture
Pachucki, M. C., & Breiger, R. L. (2010) · Annual Review of Sociology · doi:10.1146/annurev.soc.012809.102615 · Cited by 525 · see citing works
“Salganik et al. 2006) examine dynamic patterns in”
BackgroundCrowdfunding Creative Ideas: The Dynamics of Project Backers in KickstarterPreprint
Kuppuswamy, V., & Bayus, B. L. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2234765 · Cited by 544 · see citing works
“For example, people often choose music for downloading based on popularity not quality (Salganik, et al. 2006) and bidders tend to herd into online auctions with more bids even though this activity is not a signal of higher quality (Simonsohn and Ariely 2008).”
Can Large Language Models Transform Computational Social Science?
Ziems, C., Held, W., Shaikh, O., Chen, J., Zhang, Z., & Yang, D. (2024) · Computational Linguistics · doi:10.1162/coli_a_00502 · Cited by 585 · see citing works
“Since social systems evolve unpredictably (Salganik et al., 2006), simulated samples inherently have limited predictive and explanatory power.”
BackgroundHutchins, B. I., Yuan, X., Anderson, J. M., & Santangelo, G. M. (2016) · PLoS Biology · doi:10.1371/journal.pbio.1002541 · Cited by 500 · see citing works
“The results of a landmark study on the relationship between quality and success in a competitive market suggest that 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].”
BackgroundDynamics of information diffusion and its applications on complex networks
Zhang, Z., Liu, C., Zhan, X., Lu, X. B., Zhang, C., & Zhang, Y. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.07.002 · Cited by 487 · see citing works
Raafat, R. M., Chater, N., & Frith, C. (2009) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2009.08.002 · Cited by 479 · see citing works
Cheng, J., Bernstein, M. S., Danescu-Niculescu-Mizil, C., & Leskovec, J. (2017) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2998181.2998213 · Cited by 499 · see citing works
Status: Insights from Organizational Sociology
Sauder, M., Lynn, F. B., & Podolny, J. M. (2012) · Annual Review of Sociology · doi:10.1146/annurev-soc-071811-145503 · Cited by 472 · see citing works
“These effects of the formalization of status are shown as well in studies of licensing agencies (Baum & Oliver 1991), certification contests (Rao 1994), financial analysts (Zuckerman 1999), benchmarking data (Zuckerman & Sgourev 2006), and educational rankings (Sauder 2006, Espeland & Sauder 2007).”
BackgroundInfluentialThe Effect of Providing Peer Information on Retirement Savings Decisions
Beshears, J. L., Choi, J. J., Laibson, D., Madrian, B. C., & Milkman, K. L. (2015) · The Journal of Finance · doi:10.1111/jofi.12258 · Cited by 446 · see citing works
Sequential and Temporal Dynamics of Online Opinion
Godes, D., & Silva, J. (2011) · Marketing Science · doi:10.1287/mksc.1110.0653 · Cited by 445 · see citing works
“2007), music (Salganik et al. 2006), books (Chevalier and Mayzlin 2006), medical products (Zhang 2010, Manchanda et al.”
BackgroundPechenick, E. A., Danforth, C. M., & Dodds, P. S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0137041 · Cited by 437 · see citing works
Self-citationDellaPosta, D., Shi, Y., & Macy, M. (2015) · American Journal of Sociology · doi:10.1086/681254 · Cited by 440 · see citing works
“In the “music lab” study, participants randomly assigned to different groups were asked to rank songs downloaded from the Web ðSalganik, Dodds, and Watts 2006Þ.”
MethodKittur, A., Smus, B., & Kraut, R. E. (2011) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/1979742.1979902 · Cited by 434 · see citing works
“A large number of studies have examined the usefulness of MTurk as a platform for collecting and evaluating data for applications including machine translation [4] image databases [7], and natural language processing [22].”
BackgroundMethodPrediction and explanation in social systems
Hofman, J. M., Sharma, A., & Watts, D. J. (2017) · Science · doi:10.1126/science.aal3856 · Cited by 429 · see citing works
Self-citationEcho chambers and viral misinformation: Modeling fake news as complex contagion
Törnberg, P. (2018) · PLoS ONE · doi:10.1371/journal.pone.0203958 · Cited by 430 · see citing works
A framework for the unification of the behavioral sciences
Gintis, H., Ainslie, G., Arnhart, L., Bentley, R., Brown, R., Brown, S. L., Burgess, R. L., Molenaar, P., Clarke, S., Colman, A., et al. (2007) · Behavioral and Brain Sciences · doi:10.1017/s0140525x07000581 · Cited by 373 · see citing works
“As demonstrated by empirical data from modern and prehistoric societies (e.g., Bentley & Shennan 2003; Neiman 1995; Salganik et al. 2006; Shennan & Wilkinson 2001; Simkin & Roychowdhury 2003), other forms of imitation include copying within the structured constraints of a social network (e.”
BackgroundSocial Influence and the Collective Dynamics of Opinion Formation
Moussaïd, M., Kämmer, J. E., Analytis, P. P., & Neth, H. (2013) · PLoS ONE · doi:10.1371/journal.pone.0078433 · Cited by 407 · see citing works
“seems well suited to the study of opinion dynamics under controlled conditions [15].”
BackgroundInfluentialTie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment
Aral, S., & Walker, D. (2014) · Management Science · doi:10.1287/mnsc.2014.1936 · Cited by 389 · see citing works
Observational Learning: Evidence from a Randomized Natural Field Experiment
Cai, H., Chen, Y., & Fang, H. (2009) · American Economic Review · doi:10.1257/aer.99.3.864 · Cited by 369 · see citing works
A First Look at Online Reputation on Airbnb, Where Every Stay is Above Average
Zervas, G., Proserpio, D., & Byers, J. (2015) · doi:10.2139/ssrn.2554500 · Cited by 601 · see citing works
Digital Footprints: Opportunities and Challenges for Online Social Research
Golder, S. A., & Macy, M. W. (2014) · Annual Review of Sociology · doi:10.1146/annurev-soc-071913-043145 · Cited by 382 · see citing works
“As a result, we cannot rule out the possibility that demographic differences in opinions (e.g., the social liberalism of college graduates) are spuriously generated or exaggerated by the unmeasured effects of peer influence (Della Posta et al. 2013, McPherson 2004, Salganik et al. 2006).”
BackgroundCooperation and Contagion in Web-Based, Networked Public Goods Experiments
Suri, S., & Watts, D. J. (2011) · PLoS ONE · doi:10.1371/journal.pone.0016836 · Cited by 355 · see citing works
Self-citationElectronic Publication and the Narrowing of Science and Scholarship
Evans, J. A. (2008) · Science · doi:10.1126/science.1150473 · Cited by 364 · see citing works
Social Learning and Peer Effects in Consumption: Evidence from Movie Sales
Moretti, E. (2011) · The Review of Economic Studies · doi:10.1093/restud/rdq014 · Cited by 355 · see citing works
“Salganik et al. (2006) set up a web site for music 1 In this paper, I focus on consumption of movies.”
BackgroundThe convergence of social and technological networks
Kleinberg, J. M. (2008) · Communications of the ACM · doi:10.1145/1400214.1400232 · Cited by 346 · see citing works
“36 Through an online experiment in which participants were assigned to multiple, independently evolving versions of a music-download site— essentially, a set of artificially constructed “parallel universes” in which copies of the site could develop inde-pendently—Salganik et al. found that when…”
BackgroundThe small, disloyal fake news audience: The role of audience availability in fake news consumption
Nelson, J. L., & Taneja, H. (2018) · New Media & Society · doi:10.1177/1461444818758715 · Cited by 355 · see citing works
“Studies have repeatedly shown that algorithmic recommendations and social information tend to enhance inequality by increasing exposure to what is already popular (Bucher, 2017; Carlson, 2017; Napoli, 2014; Salganik et al., 2006).”
BackgroundHow Does Popularity Information Affect Choices? A Field Experiment
Tucker, C., & Zhang, J. (2011) · Management Science · doi:10.1287/mnsc.1110.1312 · Cited by 322 · see citing works
“Previous research predicts that this class enrollment information makes the strategy class more attractive, as popularity tends to be self-reinforcing (see, for example, Salganik et al. 2006, Cai et al. 2009, Zhang 2010, Chen et al. 2011).”
Mason, W. A., Conrey, F. R., & Smith, E. R. (2007) · Personality and Social Psychology Review · doi:10.1177/1088868307301032 · Cited by 335 · see citing works
“Salganik, Dodds, and Watts (2006) argued that this pattern is a result of positive feedback from social influence: As individuals influence each other, success and failure are both amplified, producing the observed pattern of unpredictable but high variation.”
Measuring the predictability of life outcomes with a scientific mass collaboration
Salganik, M. J., Lundberg, I., Kindel, A. T., Ahearn, C. E., Al-Ghoneim, K., Almaatouq, A., Altschul, D. M., Brand, J. E., Carnegie, N. B., Compton, R. J., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1915006117 · Cited by 322 · see citing works
Self-citationMeasuring the effectiveness of scientific gatekeeping
Siler, K., Lee, K. P., & Bero, L. A. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418218112 · Cited by 338 · see citing works
“Status signals and other social factors can profoundly affect diffusion processes, as well as people’s opinions and perceptions of quality (45).”
BackgroundSocial network analysis and the evaluation of leadership networks
Hoppe, B., & Reinelt, C. (2010) · The Leadership Quarterly · doi:10.1016/j.leaqua.2010.06.004 · Cited by 327 · see citing works
“collective leadership networks? The general question of what mak something contagious or popular extends beyond the s research, but Salganik et al (2006) suggest that network dynamics make popularity harder to predict than previously thought.”
BackgroundCooperation and contagion in web-based, networked public goods experiments
Suri, S., & Watts, D. J. (2011) · ACM SIGecom Exchanges · doi:10.1145/1998549.1998550 · Cited by 318 · see citing works
Self-citationAre Consumers More Likely to Contribute Online Reviews for Hit or Niche Products?
Dellarocas, C. N., Gao, G. G., & Narayan, R. (2010) · Journal of Management Information Systems · doi:10.2753/mis0742-1222270204 · Cited by 310 · see citing works
“[34], and tucker and Zhang [37] independently study the effect of the availability of popularity information (e.”
BackgroundInherent Tradeoffs in the Fair Determination of Risk Scores
Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603197 · Cited by 347 · see citing works
From Newsworthiness to Shareworthiness
Trilling, D., Tolochko, P., & Burscher, B. (2016) · Journalism & Mass Communication Quarterly · doi:10.1177/1077699016654682 · Cited by 319 · see citing works
The average laboratory samples a population of 7,300 Amazon Mechanical Turk workers
Stewart, N., Ungemach, C., Harris, A. J. L., Bartels, D. M., Newell, B. R., Paolacci, G., & Chandler, J. (2015) · Judgment and Decision Making · doi:10.1017/s1930297500005611 · Cited by 304 · see citing works
“Many factors differ across our laboratories—such as the description of tasks, duration, posting time, requester reputation, or even just randomness in early update of HITs (Salganik, Dodds, & Watts, 2006)— and so experimental manipulation of these factors is required to make causal claims.”
Social influence and the diffusion of user-created content
Bakshy, E., Karrer, B., & Adamic, L. A. (2009) · ACM Conference on Electronic Commerce (EC) · doi:10.1145/1566374.1566421 · Cited by 312 · see citing works
Kittur, A., Smus, B., Khamkar, S., & Kraut, R. E. (2011) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/2047196.2047202 · Cited by 309 · see citing works
“A large number of studies have examined the usefulness of MTurk as a platform for collecting and evaluating data for applications including machine translation [4] image databases [7], and natural language processing [22].”
BackgroundMethodComputational Social Science and Sociology
Edelmann, A., Wolff, T., Montagne, D., & Bail, C. A. (2020) · Annual Review of Sociology · doi:10.1146/annurev-soc-121919-054621 · Cited by 305 · see citing works
“In an influential study, Salganik et al. (2006) created an online “music lab” to study how peer influence shapes musical preferences of new artists.”
BackgroundOpen access, readership, citations: a randomized controlled trial of scientific journal publishing
Davis, P. M. (2011) · The FASEB Journal · doi:10.1096/fj.11-183988 · Cited by 298 · see citing works
Using a model of social dynamics to predict popularity of news
Lerman, K., & Hogg, T. (2010) · International World Wide Web Conference (WWW) · doi:10.1145/1772690.1772754 · Cited by 299 · see citing works
The Pulse of News in Social Media: Forecasting Popularity
Bandari, R., Asur, S., & Huberman, B. A. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v6i1.14261 · Cited by 296 · see citing works
Salganik, M., & Watts, D. J. (2008) · Social Psychology Quarterly · doi:10.1177/019027250807100404 · Cited by 290 · see citing works
“The tendency for the subjects to favor the least popular songs, which at first may appear surprising, is consistent with previous work (Salganik et al. 2006) and could simply be an artifact of our experimental set-up—just as the top spot on a list is salient, so too is the bottom.”
BackgroundMethodResultInfluentialSelf-citationStewardship of global collective behavior
Bak-Coleman, J. B., Alfano, M., Barfuss, W., Bergstrom, C. T., Centeno, M. A., Couzin, I. D., Donges, J. F., Galesic, M., Gersick, A. S., Jacquet, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2025764118 · Cited by 289 · see citing works
The spontaneous emergence of conventions: An experimental study of cultural evolution
Centola, D., & Baronchelli, A. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418838112 · Cited by 282 · see citing works
“Moreover, prior experimental studies have typically assumed the existence of either an explicit reward for universal coordination (15) or a mechanism that aggregates and reports the collective state of the population (17, 24), which has made it impossible to evaluate the hypothesis that global coordination is the resu…”
BackgroundA first look at online reputation on Airbnb, where every stay is above average
Zervas, G., Proserpio, D., & Byers, J. W. (2020) · Marketing Letters · doi:10.1007/s11002-020-09546-4 · Cited by 283 · see citing works
Music recommendation and discovery in the long tail
Herrada, Ò. C. (2009) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 284 · see citing works
What makes a video go viral? An analysis of emotional contagion and Internet memes
Guadagno, R. E., Rempala, D. M., Murphy, S., & Okdie, B. M. (2013) · Computers in Human Behavior · doi:10.1016/j.chb.2013.04.016 · Cited by 280 · see citing works
“In a one such study, Salganik, Dodds, and Watts (2006) created a laboratory ''music market'' online where 14,000 participants were allowed to download songs they had never been exposed to previously .”
BackgroundConsensus Decision Making by Fish
Sumpter, D. J. T., Krause, J., James, R., Couzin, I. D., & Ward, A. J. W. (2008) · Current Biology · doi:10.1016/j.cub.2008.09.064 · Cited by 276 · see citing works
“For example, economic bubbles and fashion trends are attributed to lack of independence between the individuals involved in these activities [23, 24].”
BackgroundThe Myth of Partisan Selective Exposure: A Portrait of the Online Political News Audience
Nelson, J. L., & Webster, J. G. (2017) · Social Media + Society · doi:10.1177/2056305117729314 · Cited by 267 · see citing works
Characterizing and Modeling the Dynamics of Activity and Popularity
Zhang, P., Li, M., Gao, L., Fan, Y., & Di, Z. (2014) · PLoS ONE · doi:10.1371/journal.pone.0089192 · Cited by 269 · see citing works
“It is widely believed that information can spread quickly along social links using user-to-user exchanges, also known as ‘‘word-ofmouth’’ exchanges; moreover, the users’ behaviors are strongly influenced by their neighbors [23–27].”
BackgroundWhat Makes Popular Culture Popular? Product Features and Optimal Differentiation in Music
Askin, N., & Mauskapf, M. (2017) · American Sociological Review · doi:10.1177/0003122417728662 · Cited by 264 · see citing works
“In a series of online experiments, Salganik and colleagues investigated how product quality and social influence affect success in an artificial music market (Salganik et al. 2006; Salganik and Watts 2008, 2009).”
BackgroundSocial contextual recommendation
Jiang, M., Cui, P., Liu, R. Z. Z., et al. (2012) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2396761.2396771 · Cited by 262 · see citing works
“In [25], web-based experiments are designed for music adoption prediction in an artificial music market.”
BackgroundFrom the functions of music to music preference
Schäfer, T., & Sedlmeier, P. (2009) · Psychology of Music · doi:10.1177/0305735608097247 · Cited by 252 · see citing works
Insiders, Outsiders, and the Struggle for Consecration in Cultural Fields
Cattani, G., Ferriani, S., & Allison, P. D. (2014) · American Sociological Review · doi:10.1177/0003122414520960 · Cited by 258 · see citing works
A Survey of Information Cascade Analysis
Zhou, F., Xu, X., Trajcevski, G., & Zhang, K. (2021) · ACM Computing Surveys · doi:10.1145/3433000 · Cited by 264 · see citing works
“, is it a problem of insufficient data, or is it inherently unpredictable [38, 135, 168, 173]? Is this because the innate quality of content has a minor impact, while the social influence or other external factors govern the eventual popularity of items [144, 168]? Unfortunately, there is no explicit answer applicable…”
BackgroundSpontaneous emergence of social influence in online systems
Onnela, J.-P. “., & Reed‐Tsochas, F. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0914572107 · Cited by 251 · see citing works
“Successful products in cultural markets have been found to be orders of magnitude more popular than the average cultural product (7).”
BackgroundChoosing experiments to accelerate collective discovery
Rzhetsky, A., Foster, J. G., Foster, I. T., & Evans, J. A. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1509757112 · Cited by 247 · see citing works
“In assuming that scientists’ research choices are influenced by concept degree, we posit that scientists are influenced by the choices of others, a well-attested choice heuristic (25, 26).”
BackgroundBalancing on the Creative Highwire
Berg, J. M. (2016) · Administrative Science Quarterly · doi:10.1177/0001839216642211 · Cited by 252 · see citing works
“In particular, I drew on Salganik, Dodds, and Watts (2006), who measured the quality of online content (in their case, songs) as the frequency with which audience members downloaded the songs when no other information or social encouragement was provided.”
BackgroundInfluentialCharacterizing and Modeling the Dynamics of Online Popularity
Ratkiewicz, J., Fortunato, S., Flammini, A., Menczer, F., & Vespignani, A. (2010) · Physical Review Letters · doi:10.1103/physrevlett.105.158701 · Cited by 249 · see citing works
Crowd behaviour during high-stress evacuations in an immersive virtual environment
Moussaïd, M., Kapadia, M., Thrash, T., Sumner, R. W., Groß, M. H., Helbing, D., & Hölscher, C. (2016) · Journal of The Royal Society Interface · doi:10.1098/rsif.2016.0414 · Cited by 250 · see citing works
“in concert with the recent development of computational methods in the social sciences that employ artificial environments to study the dynamics of large social systems (37), such as cultural markets (38), social networks (39), and collective problemsolving (40). Here, we extend this experimental principle to crowd be…”
BackgroundA survey on predicting the popularity of web content
Tatar, A., de Amorim, M. D., Serge Fdida, B., & Antoniadis, P. (2014) · Journal of Internet Services and Applications · doi:10.1186/s13174-014-0008-y · Cited by 243 · see citing works
“reveals that, when users are informed about the collective decisions of other individuals, the popularity of songs are driven by a “rich-get-richer” effect [87].”
BackgroundThe Simple Rules of Social Contagion
Hodas, N. O., & Lerman, K. (2014) · Scientific Reports · doi:10.1038/srep04343 · Cited by 317 · see citing works
“Explicit social feedback can significantly magnify user response, albeit making it less useful for popularizing high-quality content [28].”
BackgroundTALENT AND/OR POPULARITY: WHAT DOES IT TAKE TO BE A SUPERSTAR?
Franck, E. P., & Nüesch, S. (2010) · Economic Inquiry · doi:10.1111/j.1465-7295.2010.00360.x · Cited by 245 · see citing works
“Salganik, Dodds, and Watts (2006) use an experimental study to investigate the talent and popularity hypotheses in an artificial cultural market.”
Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes
Shen, H., Wang, D., Song, C., & Barabási, A.-Ĺ. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8739 · Cited by 319 · see citing works
The spread of fake news by social botsPreprint
Shao, C., Ciampaglia, G. L., Varol, O., Flammini, A., & Menczer, F. (2017) · arXiv (Cornell University) · Cited by 249 · see citing works
- 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
- 2,219
- References
- 21
- Citations by year
- 2026: 117 · 2025: 89 · 2024: 98 · 2023: 127 · 2022: 109 · 2021: 244 · 2020: 106 · 2019: 130 · 2018: 129 · 2017: 144 · 2016: 128 · 2015: 126 · 2014: 141 · 2013: 92 · 2012: 125
- FWCI
- 162.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Safety Research › 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
