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 701–800 of 2,217
How social learning adds up to a culture: from birdsong to human public opinion
Tchernichovski, O., Fehér, O., Fimiarz, D., & Conley, D. (2017) · Journal of Experimental Biology · doi:10.1242/jeb.142786 · Cited by 22
“Semantic coordination can stem from dyadic interactions (Garrod and Anderson, 1987), but shared social conventions may also emerge from centralized authority, social leadership and aggregated information (Kearns et al., 2009; Salganik et al., 2006).”
BackgroundMedia fragmentation in the context of bounded social networks: How far can it go?
Riles, J. M., Pilny, A. N., & Tewksbury, D. H. (2017) · New Media & Society · doi:10.1177/1461444817696242 · Cited by 25
“For instance, Salganik et al. (2006) observe that an awareness of the musical preferences of other consumers is associated with consumption of content in line with those perceived environmental preferences.”
BackgroundComputation and the Sociological Imagination
Evans, J., & Foster, J. G. (2019) · Contexts · doi:10.1177/1536504219883850 · Cited by 23
Yan, B., Jian, L., Ren, R., Fulk, J. L., Sidnam-Mauch, E., & Monge, P. R. (2020) · Communication Research · doi:10.1177/0093650220915033 · Cited by 23
“As a result, crowd members provide less diverse predictions compared to when working independently, leading to herding and biases in their judgments (Hong et al., 2020; Lorenz et al., 2011; Muchnik et al., 2013; Salganik et al., 2006; Salganik & Watts, 2009; Toyokawa et al., 2018).”
BackgroundThe relationship between uses of music, musical taste, age, and life goals
Hird, E., & North, A. C. (2020) · Psychology of Music · doi:10.1177/0305735620915247 · Cited by 24
Bursell, M., & Olsson, F. (2021) · Poetics · doi:10.1016/j.poetic.2021.101549 · Cited by 23
The Role of the Organization Structure in the Diffusion of Innovations
Sáenz‐Royo, C., Gracia-Lázaro, C., & Moreno, Y. (2015) · PLoS ONE · doi:10.1371/journal.pone.0126076 · Cited by 35
“Each person in the organization has attached a likelihood of accepting the innovation which increases with the positive externalities (which are attributed to network effects [25], coordination games [26], learning from others [27], social pressure [28] and trust [29]) resulting from the pressure to adopt a favorable…”
BackgroundIntelligent career planning via stochastic subsampling reinforcement learning
Guo, P., Xiao, K., Ye, Z., Zhu, H., & Zhu, W. (2022) · Scientific Reports · doi:10.1038/s41598-022-11872-8 · Cited by 34
“48 suggested that popularity is positively related to the quality of a company, and people are more willing to work in popular companies.”
BackgroundThe Glass Wall and the Gendered Evaluation of Role Expansion in Freelancing Careers
Lee, Y. G., Koval, C. Z., & Lee, S. S. (2022) · Academy of Management Journal · doi:10.5465/amj.2020.1176 · Cited by 24
The Benefits of Social Influence in Optimized Cultural Markets
Abeliuk, A., Berbeglia, G., Cebrián, M., & Hentenryck, P. V. (2015) · PLoS ONE · doi:10.1371/journal.pone.0121934 · Cited by 23
“In their seminal paper [8], Salganik, Dodds, and Watts offer a satisfactory explanation for this paradox: The impact of social influence on the behavior of individuals (word-of-mouth), a process a priori invisible to experts, may distort the quality perceived by the customers, making quality and popularity out of sync…”
BackgroundMethodInfluentialSocial Feedback Influences Musically Induced Emotions
Egermann, H., Grewe, O., Kopiez, R., & ALTENMÜLLER, E. O. (2009) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2009.04789.x · Cited by 23
Universality in movie rating distributions
Lorenz, J. (2009) · The European Physical Journal B · doi:10.1140/epjb/e2009-00283-3 · Cited by 33
Clément, M., Hille, A., Lücke, B., Schmidt-Stölting, C., & Sambeth, F. (2008) · Schmalenbach Journal of Business Research · doi:10.1007/bf03372813 · Cited by 23
Enabling Large-Scale Deliberation Using Attention-Mediation MetricsPreprint
Klein, M. H. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1837707 · Cited by 23
An Empirical Analysis of a Crowdfunding PlatformPreprint
Kim, J., Newberry, P. W., & Qiu, C. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2672075 · Cited by 22
“Salganik et al. (2006) perform an experiment where a menu of songs is presented either randomly or by the order of popularity and conclude that success was only partly determined by quality (see also Hendricks et al., 2012 for a similar conclusion).”
Characterizing and modeling popularity of user-generated videos
Borghol, Y. (2022) · UNSWorks (University of New South Wales, Sydney, Australia) · doi:10.26190/unsworks/15892 · Cited by 23
How rediscovering nodality can improve democratic governance in a digital world
Margetts, H. Z., & John, P. C. (2023) · Public Administration · doi:10.1111/padm.12960 · Cited by 33
An analysis of popularity information effects: Field experiments in an online marketplace
Yoo, B., Jeon, S., & Han, T. (2016) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2016.03.003 · Cited by 23
“The popularity information normally reinforces current sales trends by encouraging customers to choose popular products; consequently, it has a tendency to be self-reinforcing (Salganik et al., 2006; Zhang, 2010; Wang and Xie, 2011).”
BackgroundBias and High-Dimensional Adjustment in Observational Studies of Peer Effects
Eckles, D., & Bakshy, E. (2020) · Journal of the American Statistical Association · doi:10.1080/01621459.2020.1796393 · Cited by 40
“, 2013), or subsequent exposure to peer behaviors (Aral and Walker, 2011; Bakshy et al., 2012a; Salganik et al., 2006) have been possible, but in many cases these experimental designs are infeasible.”
BackgroundThreshold Models of Collective Behavior II: The Predictability Paradox and Spontaneous Instigation
Macy, M. W., & Evtushenko, A. (2020) · Sociological Science · doi:10.15195/v7.a26 · Cited by 21
Share of Attention: Exploring the Allocation of User Attention to Consumer Applications
Rieser, L., & Furneaux, B. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.107006 · Cited by 22
Analytical sociology amidst a computational social science revolution
Jarvis, B. F., Keuschnigg, M., & Hedström, P. (2021) · Routledge eBooks · doi:10.4324/9781003024583-4 · Cited by 24
Balancing Categorical Conventionality in Music
Silver, D., Childress, C., Lee, M. M., Slez, A., & Dias, F. (2022) · American Journal of Sociology · doi:10.1086/719937 · Cited by 21
“For example, the median band has three fans and 120 plays.3 As music in 2007 was not available for sale on or through MySpace, like Salganik, Dodds, and Watts (2006) we are able to measure a relatively pure form of popularity that is not conditioned on factors like pricing, variation in disposable…”
BackgroundBautista, P., de Escoriaza, J. C., Sánchez, E. V., Salinas, A. C., & Hernández, S. O. (2022) · Computers & Education · doi:10.1016/j.compedu.2022.104594 · Cited by 32
The Elements of Cultural Power: Novelty, Emotion, Status, and Cultural Capital
Zhou, D. (2022) · American Sociological Review · doi:10.1177/00031224221123030 · Cited by 22
“…the best available proxy for the experience of cultural power, despite the assumptions it entails, as researchers only have access to outcomes of people’s cognitive process (e.g., likes, shares, consumption) (Askin and Mauskapf 2017; Bail 2016a; Casas and Williams 2019; Salganik et al. 2006).”
BackgroundA Microstructural Approach to Self-Organizing: The Emergence of Attention Networks
Tonellato, M., Tasselli, S., Conaldi, G., Lerner, J., & Lomi, A. (2023) · Organization Science · doi:10.1287/orsc.2023.1674 · Cited by 22
The Traditional Future: A Computational Theory of Library Research
Abbott, A. (2008) · College & Research Libraries · doi:10.5860/crl.69.6.524 · Cited by 22
Biological and Cultural Evolution: Similar but Different
Mesoudi, A. (2007) · Biological Theory · doi:10.1162/biot.2007.2.2.119 · Cited by 22
“in press), to rapidly changing, horizontally transmitted fads and fashions such as tastes in pop music (Salganik et al. 2006; Bentley et al. 2007).”
BackgroundTowards Identifying the Challenges Associated with Emerging Large Scale Social Networks
Malik, H., & Malik, A. S. (2011) · Procedia Computer Science · doi:10.1016/j.procs.2011.07.059 · Cited by 22
Diversity of User Activity and Content Quality in Online Communities
Hogg, T., & Szabo, G. (2009) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v3i1.13940 · Cited by 22
“At the other extreme, perceived value could be largely driven by popularity among the users, or subgroups of users, as seen in some cultural products (Salganik, Dodds, and Watts 2006).”
BackgroundA social science‐inspired complexity policy: Beyond the mantra of incentivization
Squazzoni, F. (2014) · Complexity · doi:10.1002/cplx.21520 · Cited by 22
The H-index paradox: your coauthors have a higher H-index than you do
Benevenuto, F., Laender, A. H. F., & Alves, B. L. (2015) · Scientometrics · doi:10.1007/s11192-015-1776-2 · Cited by 21
“As a consequence, the friendship paradox can dramatically skew an individual’s local observation, making such an observation appear far more common than it is in reality (Centola 2010; Salganik et al. 2006).”
The Cost of Collaboration for Code and Art: Evidence from a Remixing CommunityPreprint
Hill, B. M., & Monroy‐Hernández, A. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.01297 · Cited by 22
“Previous work has shown that explicit and public expression of admiration may create self-git revision 4ac6342 on 2012/12/12 reinforcing social dynamics [ 37 ] .”
BackgroundFrom Nudge to Culture and Back Again: Coalface Governance in the Regulated Organization
Huising, R., & Silbey, S. (2018) · Annual Review of Law and Social Science · doi:10.1146/annurev-lawsocsci-110615-084716 · Cited by 15
Oyedele, A., & Goenner, E. (2020) · Journal of Consumer Behaviour · doi:10.1002/cb.1900 · Cited by 22
Christensen, H. S., Helimäki, T., Mattila, M., & von Schoultz, Å. (2021) · Political Research Exchange · doi:10.1080/2474736x.2021.1892456 · Cited by 21
“…shown that social media endorsements impact various types of behaviours such as news consumption (Knobloch-Westerwick et al. 2005; Lerman 2007; Messing and Westwood 2014), music consumption (Salganik, Dodds, and Watts 2006), and course preferences among college students (Ste ff es and Burgee 2009).”
BackgroundThe influence of launching mobile channels on online customer reviews
Kim, J. M., Lee, E., & Mariani, M. M. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2021.08.048 · Cited by 21
“Similar findings regarding the influence of social norms are found in various contexts, including retirement fund decisions (Duflo and Saez, 2002), restaurant selection (Cai et al., 2009), and music downloads (Salganik et al., 2006).”
ResultSampling Bias in Entrepreneurial Experiments
Cao, R., Koning, R. M., & Nanda, R. (2023) · Management Science · doi:10.1287/mnsc.2021.01740 · Cited by 21
“…tepid consumer interest can drive negative social learning dynamics that stunt consumer demand and startup growth, especially for the many socially enabled and networked products that are launched on Product Hunt (Boudreau 2021; Moretti 2011; Salganik, Dodds, and Watts 2006; Koning and Model 2013).”
BackgroundSignaling and herding in reward-based crowdfunding
Garnica, G. R., Urtiaga, M. G., & Tribó, J. A. (2024) · Small Business Economics · doi:10.1007/s11187-024-00933-z · Cited by 21
“There is also evidence of inefficient follower behavior in markets for music downloads (Salganik et al., 2006) and, even more closely related to our study, in equity crowdfunding, where amateur (but self-confident) funders are more likely to disregard objec-tive quality signals and go with the…”
Rafalak, M., Abramczuk, K., & Wierzbicki, A. (2014) · International World Wide Web Conference (WWW) · doi:10.1145/2567948.2578997 · Cited by 21
“[12] studied music review systems and these are just few examples representing a wide branch of research.”
BackgroundGeneric Aversion and Observational Learning in the Over-the-Counter Drug Market
Carrera, M., & Villas‐Boas, S. B. (2023) · American Economic Journal Applied Economics · doi:10.1257/app.20200867 · Cited by 20
“…effects of peer usage disclosure, it is often difficult to disentangle whether these effects are driven by updated quality priors or by social channels, such as status or network effects (see for example, Cai et al. (2009) in restaurants; Salganik et al. (2006) in an artificial music market).”
Social complex contagion in music listenership: A natural experiment with 1.3 million participants
Ternovski, J., & Yasseri, T. (2019) · Social Networks · doi:10.1016/j.socnet.2019.10.005 · Cited by 20
“However, it has been argued that social influence makes these markets unpredictable (14).”
BackgroundThe influence of user-generated content on video game demand
Brunt, C. S., King, A. S., & King, J. T. (2019) · Journal of Cultural Economics · doi:10.1007/s10824-019-09349-0 · Cited by 21
Antecedents and consequences of attitude contagion processes: the example of apparel brand fan pages
Hsu, L. (2019) · Journal of Product & Brand Management · doi:10.1108/jpbm-07-2018-1930 · Cited by 24
Predicting and Understanding News Social Popularity with Emotional Salience Features
Gupta, R. K., & Yang, Y. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3351048 · Cited by 23
“In a broader domain of interest, it has been found that the behavioral dynamics involved in the distribution and diffusion of news is similar to the distribution and diffusion of creative content such as songs [32] or viral messages [19].”
BackgroundCharacteristics and evolution of content popularity and user relations in social networks
Canali, C., Colajanni, M., & Lancellotti, R. (2010) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc.2010.5546710 · Cited by 21
“For example, [15] shows that, in e-commerce systems, the knowledge of the other customers choice affects the collective decision of large groups of users.”
BackgroundVideo Killed the Radio Star? Online Music Videos and Recorded Music SalesPreprint
Kretschmer, T., & Peukert, C. (2014) · SSRN Electronic Journal · doi:10.2139/ssrn.2425386 · Cited by 20
Did Einstein really say that? Testing content versus context in the cultural selection of quotationsPreprint
Acerbi, A., & Tehrani, J. J. (2017) · doi:10.31235/osf.io/x8db2 · Cited by 21
The Externalities of Exploration and How Data Diversity Helps Exploitation
Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603208 · Cited by 20
Information diffusion in signed networks
He, X., Du, H., Feldman, M. W., & Li, G. (2019) · PLoS ONE · doi:10.1371/journal.pone.0224177 · Cited by 19
Gender differences in risk behavior and the link to household effects and individual wealth
Khor, L., Sariyev, O., & Loos, T. K. (2020) · Journal of Economic Psychology · doi:10.1016/j.joep.2020.102266 · Cited by 21
Anticipating Attention: On the Predictability of News Headline Tests
Hagar, N., Diakopoulos, N. A., & DeWilde, B. (2021) · Digital Journalism · doi:10.1080/21670811.2021.1984266 · Cited by 24
“This inconsistency is driven by factors that cannot be observed through content alone, such as social influence within groups or selective attention heuristics (Salganik, Dodds, and Watts 2006; Zillmann et al. 2004).”
BackgroundGatekeeping and the Use of Contested Practices in Creative Industries: The Case of Fur in Fashion
Godart, F. C., Hsu, G., & Negro, G. (2022) · Organization Science · doi:10.1287/orsc.2022.1591 · Cited by 19
The public sphere as a dynamic network
Friemel, T. N., & Neuberger, C. (2023) · Communication Theory · doi:10.1093/ct/qtad003 · Cited by 26
Meiseberg, B. (2012) · Journal of Cultural Economics · doi:10.1007/s10824-012-9196-0 · Cited by 20
Yang, Y., & Coffey, A. J. (2014) · The International Journal on Media Management · doi:10.1080/14241277.2014.943899 · Cited by 20
The Long Tail in Recommender Systems
Celma, Ò. (2010) · Springer eBooks · doi:10.1007/978-3-642-13287-2_4 · Cited by 22
Six Predictions about the Decision Making of Animal and Human Groups
Sumpter, D. J. T., Zabzina, N., & Nicolis, S. C. (2012) · Managerial and Decision Economics · doi:10.1002/mde.2553 · Cited by 20
Hegemony in Social Media and the effect of recommendations
Stoica, A.-A., & Chaintreau, A. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308560.3317589 · Cited by 20
Kovács, B., & Horwitz, S. R. (2020) · Strategic Organization · doi:10.1177/1476127020922482 · Cited by 21
Schemas, Interactions, and Objects in Meaning‐Making1
Rawlings, C. M., & Childress, C. (2021) · Sociological Forum · doi:10.1111/socf.12759 · Cited by 22
Li, W., Germine, L. T., Mehr, S. A., Srinivasan, M., & Hartshorne, J. K. (2022) · Infant and Child Development · doi:10.1002/icd.2348 · Cited by 21
“Salganik et al. (2006) found distinct effects of music quality and perceived popularity on actual popularity by randomly assigned 14,000 participants to distinct music communities on a music streaming site.”
BackgroundStochastic trust network enriched by similarity relations to enhance trust-aware recommendations
Ghavipour, M., & Meybodi, M. R. (2018) · Applied Intelligence · doi:10.1007/s10489-018-1289-9 · Cited by 20
Cooperation dynamics in networked geometric Brownian motion
Stojkoski, V., Utkovski, Z., Basnarkov, L., et al. (2019) · Physical review. E · doi:10.1103/physreve.99.062312 · Cited by 20
Stochastic Models of User-Contributory Web SitesPreprint
Hogg, T., & Lerman, K. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0904.0016 · Cited by 20
“Nevertheless, the relations seen with stochastic models ca n uggest ways to improve the behavior which could be tested, either directly through experimenta l m nipulation of the web site [19] or through smaller-scale experiments [8].”
BackgroundEstimating Network Effects Using Naturally Occurring Peer Notification Queue CounterfactualsPreprint
Tutterow, C., & Saint-Jacques, G. (2019) · arXiv.org · doi:10.48550/arxiv.1902.07133 · Cited by 21
The ‘story’ of digital excess in revolutions of the Arab Spring
Ray, T. (2011) · Journal of Media Practice · doi:10.1386/jmpr.12.2.189_1 · Cited by 19
Greenberg, J. (2021) · Organization Science · doi:10.1287/orsc.2020.1416 · Cited by 20
Chen, B., & Althuizen, N. (2022) · Journal of Product Innovation Management · doi:10.1111/jpim.12621 · Cited by 19
Correlation function for generalized Pólya urns: Finite-size scaling analysis
Mori, S., & Hisakado, M. (2015) · Physical Review E · doi:10.1103/physreve.92.052112 · Cited by 19
“Web-based experimental systems s hould be developed to study the asymptotic behavior [25].”
BackgroundThe Evolution of Online Social Networks: A tutorial survey
Leberknight, C. S., İnaltekin, H., Chiang, M., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2011.943158 · Cited by 19
“To investigate the issue of inequality and unpredictability in markets, Salganik et al. [9] conducted two Web experiments in which 48 songs from previously unknown bands were presented to more than 14,000 participants.”
BackgroundMethodInfluentialMeasuring the diffusion of marketing messages across a social network
Rogers, M. L., Chapman, C., & Giotsas, V. (2012) · Journal of Direct Data and Digital Marketing Practice · doi:10.1057/dddmp.2012.25 · Cited by 19
Ross, T. L., & Weaver, A. J. (2012) · Journal of Media Psychology Theories Methods and Applications · doi:10.1027/1864-1105/a000068 · Cited by 19
Schoeffler, M., Stöter, F.-R., Bayerlein, H., et al. (2013) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1417942 · Cited by 19
Newman, M. E. J. (2018) · Oxford University Press eBooks · doi:10.1093/oso/9780198805090.003.0007 · Cited by 25
Can Interface Cues Nudge Modeling of Food Consumption? Experiments on a Food-Ordering Website
Hou, J. (2017) · Journal of Computer-Mediated Communication · doi:10.1111/jcc4.12190 · Cited by 19
Wang, C., & Zhu, J. J. H. (2019) · Telematics and Informatics · doi:10.1016/j.tele.2019.03.001 · Cited by 21
Gender-specific preference in online dating
Su, X., & Hu, H. (2019) · EPJ Data Science · doi:10.1140/epjds/s13688-019-0192-x · Cited by 21
A Poisson Factorization Topic Model for the Study of Creative Documents (and Their Summaries)
Toubia, O. (2020) · Journal of Marketing Research · doi:10.1177/0022243720943209 · Cited by 16
“On the other hand, according to Salganik, Dodds, and Watts (2006), the content of entertainment products has little impact on the success of these products, echoing previous research by Bielby and Bielby (1994), who also report a quote from a past president of CBS entertainment that “all hits are…”
BackgroundThe Limits of Popularity-Based Recommendations, and the Role of Social Ties
Bressan, M., Leucci, S., Panconesi, A., Raghavan, P., & Terolli, E. (2016) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2939672.2939797 · Cited by 23
“Will unknown books become hits and vice-versa? How and how much will the habit of the typical reader change? A large body of work has investigated questions of this kind at the individual, user level [8, 25, 26, 35].”
BackgroundInfluentialVideo Killed the Radio Star? Online Music Videos and Recorded Music Sales
Kretschmer, T., & Peukert, C. (2020) · Information Systems Research · doi:10.1287/isre.2019.0915 · Cited by 83
Tarko, V., & Aligică, P. D. (2011) · Futures · doi:10.1016/j.futures.2011.06.008 · Cited by 18
“The market approach, although it is indeed a very effective method of aggregating local knowledge [16,17,21], should not be overly idealized as it is affected by random path-dependency biases [22,23] and it cannot thus deliver truly objective results.”
BackgroundPositivity Bias in Customer Satisfaction Ratings
Park, K., Cha, M., & Rhim, E. (2018) · Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18 · doi:10.1145/3184558.3186579 · Cited by 19
“Second is the social influence bias, where new raters are influenced by existing ratings and, thereby, existing positive ratings dramatically affect future ratings [34].”
BackgroundThe behavioural economics of culture
Coate, B., & Hoffmann, R. F. (2021) · Journal of Cultural Economics · doi:10.1007/s10824-021-09419-2 · Cited by 22
The Effects of Consumer Preference and Peer Influence on Trial of an Experience Good
Pyo, T.-H., Lee, J. Y., & Park, H. M. (2022) · Journal of Marketing Research · doi:10.1177/00222437221093603 · Cited by 18
Zhou, K., Wilson, T., Starbird, K., & Spiro, E. S. (2023) · ACM Transactions on Social Computing · doi:10.1145/3577213 · Cited by 18
“In the context of social influence, experiments have shown that individual behaviors toward music and books follow the preferences of those before them [60, 66].”
BackgroundCultural evolution of music and language
Ozaki, Y., Kloots, M. D. H., Ravignani, A., & Savage, P. E. (2026) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780192894700.013.0028 · Cited by 18
Greenberg, J., Sands, D. B., Cattani, G., & Porac, J. F. (2023) · Strategic Management Journal · doi:10.1002/smj.3545 · Cited by 20
Users’ engagement in WeChat-based support communities: A multilevel perspective
Sun, M., & Sun, S. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103604 · Cited by 19
Quantifying the vulnerabilities of the online public square to adversarial manipulation tactics
Truong, B. T., Lou, X., Flammini, A., & Menczer, F. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae258 · Cited by 28
“Exceptions have considered the cost of learning about quality (70), distortions of quality assessments that result from aggregate knowledge of peer choices (56), and confirmation bias in the spread of misinformation (71).”
BackgroundSimple model for multiple-choice collective decision making
Lee, C. H., & Lucas, A. (2014) · Physical Review E · doi:10.1103/physreve.90.052804 · Cited by 19
“Given that experiments [17] suggest the latter is relevant in real world decision making, this is a natural question to study.”
BackgroundSeeing culture through the eye of the beholder: four methods in pursuit of taste
Mears, A. (2014) · Theory and Society · doi:10.1007/s11186-014-9217-4 · Cited by 17
“…and judgments when making aesthetic deliberations—in everything from the choice of their baby’s name (Lieberson 2000) to their tastes for music (Salganik et al. 2006) – yet somehow everyone happens to decide similarly at the same time, thus establishing observable patterns in taste among…”
BackgroundThe Crowdsourcing Design Space
Sakamoto, Y., Tanaka, Y., Yu, L., & Nickerson, J. V. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21852-1_41 · Cited by 18
“Specifically, crowds can participate in experiments: crowds can be simultaneously assembled in both control and experimental conditions, so the effects of social networks, organizational structure, and information flow can be studied (e.g., [10, 11]).”
BackgroundMotivation in a Social Context
Fishbach, A., Steinmetz, J., & Tu, Y. (2016) · Advances in motivation science · doi:10.1016/bs.adms.2015.12.005 · Cited by 25
Constructing a Watts-Strogatz network from a small-world network with symmetric degree distribution
Menezes, M. B. C., Kim, S., & Huang, R. (2017) · PLoS ONE · doi:10.1371/journal.pone.0179120 · Cited by 18
Spread of Employee Engagement in a Large Organizational Network
Mitra, T., Muller, M. J., Shami, N. S., Golestani, A., & Masli, M. (2017) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3134716 · Cited by 22
“Economists and market researchers have found that cultural markets are driven by social influence [72] and that social influence affects the rate of product adoption [85].”
BackgroundSelection Problems in the Presence of Implicit Bias
Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603209 · Cited by 21
Identification and impact of discoverers in online social systems
Medo, M., Mariani, M. S., Zeng, A., et al. (2017) · reroDoc Digital Library · Cited by 18
Risi, J., Sharma, A., Shah, R., Connelly, M., & Watts, D. J. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0620-8 · Cited by 21
Self-citationCats and Captions vs. Creators and the Clock
Hessel, J., Lee, L., & Mimno, D. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052684 · Cited by 30
“[52] than any of the three image-sharing settings described by Khosla et al.”
Influential
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
