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.
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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 901–1,000 of 2,217
Seguin, C., Julien, C., & Zhang, Y. (2020) · Poetics · doi:10.1016/j.poetic.2020.101501 · Cited by 15
“Salganik and colleagues, for instance, show that experimental subjects are more likely to download and listen to songs that they can see are more popular among fellow subjects (Salganik et al., 2006).”
BackgroundAN EMPIRICAL EXAMINATION OF THE ANTECEDENTS OF CONTRIBUTION PATTERNS IN CROWDFUNDED MARKETS
Burtch, G., Ghose, A., & Wattal, S. (2011) · Cited by 14
Cultural evolution: The case of babies’ first names
Xi, N., Zhang, Z., Zhang, Y., Ge, Z., Li, S., & Zhang, K. (2014) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2014.03.042 · Cited by 14
Cooperation under Indirect Reciprocity and Imitative Trust
Saavedra, S., Smith, D. A. B., & Reed‐Tsochas, F. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013475 · Cited by 16
“Social experiments have shown that the actions and opinions of others used as a proxy for quality or reputation can affect someone’s popularity or commitment to cooperate [18,24].”
Challenges in the Analysis of Online Social Networks: A Data Collection Tool Perspective
Goswami, A., & Kumar, A. (2017) · Wireless Personal Communications · doi:10.1007/s11277-017-4712-3 · Cited by 14
“There are several studies showing how sentiment analysis of OSN content helps in determining the approval of different brands by the customers [227, 228], indicators of happiness among population as an impact of an event like economic recession [229, 230] etc.”
BackgroundInstitutionalizing Authenticity in the Digitized World of Music
Askin, N., & Mol, J. M. (2018) · Emerald Group Publishing Limited eBooks · doi:10.1108/s0733-558x20180000055007 · Cited by 14
Big Data in soziologischer Perspektive
Weyer, J., Delisle, M., Kappler, K. E., Kiehl, M., Merz, C., & Schrape, J.-F. (2018) · Technikzukünfte, Wissenschaft und Gesellschaft · doi:10.1007/978-3-658-21665-8_2 · Cited by 15
Institutional change and the limitations of consumer activism
Reese, J. (2020) · Humanities and Social Sciences Communications · doi:10.1057/s41599-020-0405-8 · Cited by 14
Physical space and long-tail markets
Bentley, R. A., Madsen, M. E., & Ormerod, P. (2008) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2008.11.009 · Cited by 13
“Such markets are characterised by randomness and inherent unpredictability [ 34 ].”
BackgroundInfluentialGovernance of complex systems: results of a sociological simulation experiment
Adelt, F., Weyer, J., & Fink, R. D. (2014) · Ergonomics · doi:10.1080/00140139.2013.877598 · Cited by 14
Modelling Movement of Stock Market Indexes with Data from Emoticons of Twitter Users
Porshnev, A., Redkin, I., & Karpov, N. (2015) · Communications in computer and information science · doi:10.1007/978-3-319-25485-2_10 · Cited by 14
“This point can be supported by observing the informational cascades phenomenon in the stock market [7–9].”
BackgroundThe Influence of Collective Opinion on True-False Judgment and Information-Sharing DecisionPreprint
Li, H., & Sakamoto, Y. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2210742 · Cited by 14
Information Cascades and Social LearningPreprint
Bikhchandani, S., Hirshleifer, D., Tamuz, O., & Welch, I. (2021) · Social Science Research Network · doi:10.2139/ssrn.3851678 · Cited by 102
Behavioral Economics, Consumption, and Environmental ProtectionPreprint
Sunstein, C. R. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2296015 · Cited by 20
“These points have strong implications for consumer behavior and public policy in general (Salganik et al., 2006) and in the environmental area in particular.”
A factual and perceptional framework for assessing diversity effects of online recommender systems
Matt, C., Heß, T., & Weiß, C. H. (2019) · Internet Research · doi:10.1108/intr-06-2018-0274 · Cited by 14
Shi, Y. L. Z. (2022) · Administrative Science Quarterly · doi:10.1177/00018392221143779 · Cited by 15
The impact of early debut on scientists: Evidence from the Young Scientists Fund of the NSFC
Zhang, W., Wang, X., Chen, H., & Liu, J. (2023) · Research Policy · doi:10.1016/j.respol.2023.104935 · Cited by 16
Anwar, M. S., Schoenebeck, G. R., & Dhillon, P. S. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645497 · Cited by 14
“For instance, Salganik et al. [18] used an experimental method allowing participants to listen to a song and decide to download it based on its popularity.”
MethodFinding Event-Specific Influencers in Dynamic Social Networks
Schenk, C. B., & Sicker, D. (2011) · International Conference on Social Computing · doi:10.1109/passat/socialcom.2011.100 · Cited by 11
Identification and impact of discoverers in online social systems
Medo, M., Mariani, M. S., Zeng, A., & Zhang, Y. (2016) · Scientific Reports · doi:10.1038/srep34218 · Cited by 16
Dodds, P. S., Minot, J. R., Arnold, M., Alshaabi, T., Adams, J. L., Dewhurst, D. R., Reagan, A. J., & Danforth, C. M. (2022) · Journal of Quantitative Description Digital Media · doi:10.51685/jqd.2022.004 · Cited by 18
Self-citationModelling and analysis of tagging networks in Stack Exchange communities
Fu, X., Yu, S., & Benson, A. R. (2019) · Journal of Complex Networks · doi:10.1093/comnet/cnz045 · Cited by 13
“While such studies provide valuable insights into real-world social systems, it is also well known that there can be randomness in the evolution of social networks when crafted in a controlled setting [7].”
BackgroundHan, M. C. (2021) · International Review on Public and Nonprofit Marketing · doi:10.1007/s12208-020-00271-2 · Cited by 13
Perry, B. L., & Roth, A. R. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.031 · Cited by 16
Dynamic patterns of academic forum activities
Zhao, Z.-D., Gao, Y.-C., Cai, S., et al. (2016) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2016.05.033 · Cited by 13
Dadgar, M., & Hamzeh, A. (2022) · IEEE Access · doi:10.1109/access.2022.3147955 · Cited by 13
“Furthermore, sociological studies have proved that social influence can affect users’ decisions [8].”
BackgroundNovelty and cultural evolution in modern popular music
O’Toole, K., & Horvát, E.-Á. (2023) · EPJ Data Science · doi:10.1140/epjds/s13688-023-00377-7 · Cited by 13
“In Salganik et al’s study on social influence in cultural markets, different social behaviors led to different patterns of success within an artificial music market, demonstrating the significant impact of social influence on what songs become popular [51].”
BackgroundCan social information retrieval enhance the discovery and reuse of digital educational content?
Vuorikari, R. (2007) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/1297231.1297276 · Cited by 13
“Similar to the set up in [10], these subjects have no social clues from other users in terms of what resources to bookmark.”
BackgroundInfluentialCollective Environmental Virtue
Clowney, D. (2014) · Environmental Values · doi:10.3197/096327114x13947900181194 · Cited by 13
Improving Donation Distribution for Crowdfunding: An Agent-Based Model
Lee, Y.-C., Yen, C., & Fu, W. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-39931-7_1 · Cited by 13
“This is consistent with previous studies that show that social information systems that rely on user dynamics to distribute resources may often lead to higher inequality and unpredictability [6].”
ResultDenrell, J. C., Liu, C., & Mens, G. L. (2017) · Strategy Science · doi:10.1287/stsc.2017.0025 · Cited by 14
Is It a Masterpiece? Social Construction and Objective Constraint in the Evaluation of Excellence
Sgourev, S. V., & Althuizen, N. (2017) · Social Psychology Quarterly · doi:10.1177/0190272517738092 · Cited by 13
“The main contribution of our paper relates to recent efforts to ascertain objective constraints of social construction (Lynn et al. 2016; Salganik et al. 2006; Salganik and Watts 2008).”
BackgroundResultInfluentialAlgorithmic Distortion of Informational Landscapes
Roth, C. (2019) · Intellectica Revue de l Association pour la Recherche Cognitive · doi:10.3406/intel.2019.1895 · Cited by 13
“The pioneering work of Salganik et al. (2006) demonstrated how peer influence mediated by algorithmic rankings may modify the judgments on cultural quality.”
BackgroundOptimal use of simplified social information in sequential decision-making
Mann, R. P. (2021) · Journal of The Royal Society Interface · doi:10.1098/rsif.2021.0082 · Cited by 15
“For example: a navigating bird may choose its direction of flight in part by following others in the flock [3, 4]; foraging animals can use the presence of others at a site as an indicator of food availability [5, 6]; fish can effectively avoid predators by following others who are seeking to do the same [7, 8]; and d…”
BackgroundInfluentialThe effects of reputation on inequality in network cooperation games
Tsvetkova, M. (2021) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2020.0299 · Cited by 14
Learning from Collective Behavior.
Kearns, M. J., & Wortman, J. R. (2008) · Cited by 13
Emergence of hysteresis loop in social contagions on complex networks
Su, Z., Wang, W., Li, L., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-06286-w · Cited by 13
“Equations (8)–(10) describe the evolution of the model.”
BackgroundInfluentialDual influence embedded social recommendation
Zhang, Q., Wu, J. X., Zhang, Q., Zhang, P., Long, G., & Zhang, C. (2017) · World Wide Web · doi:10.1007/s11280-017-0486-5 · Cited by 13
“Indeed, social influence performs better in user interaction evaluation than similarity, especially in decision making and opinion changing [8, 38], therefore relying only on directly connected relationships (such as friends) to infer could impair recommendation performance.”
BackgroundTRADITION AND FASHION IN CONSUMER CHOICE: BAGGING THE SCOTTISH MUNROS
Bentley, R. A., & Ormerod, P. (2009) · Scottish Journal of Political Economy · doi:10.1111/j.1467-9485.2009.00489.x · Cited by 13
“Such markets are characterised by randomness and inherent unpredictability (Salganik et al., 2006).”
Vinge, V. (2006) · Nature · doi:10.1038/440411a · Cited by 13
Hessel, J., Lee, L., & Mimno, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.01725 · Cited by 13
“[52] than any of the three image-sharing settings described by Khosla et al.”
InfluentialExpressions of Doubt in Online News Discussions
Evans, A. M., Stavrova, O., Rosenbusch, H., & Brandt, M. J. (2023) · Social Science Computer Review · doi:10.1177/08944393211034163 · Cited by 12
“In online discussions, the majority of usergenerated content receives very little attention and it is very difficult to predict, a priori, what content will become popular (Salganik et al., 2006; Salganik & Watts, 2009).”
BackgroundChen, X., & Xiao, L. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1623730 · Cited by 17
Coda - The past and future of analytical sociology
Hedström, P. (2021) · Edward Elgar Publishing eBooks · doi:10.4337/9781789906851.00035 · Cited by 13
Seo, Y., Kim, H., Ko, Y., Yoon, H. J., Han, J. Y., Lee, J., & Seo, J. K. (2023) · Journal of Marketing Communications · doi:10.1080/13527266.2023.2235753 · Cited by 12
“Previous findings suggest that the popularity of media content can lead to favorable evaluation of that content (e.g., Salganik, Dodds, and Watts 2006; Tucker and Zhang 2011).”
Winner-Take-All? Visibility, Availability, and Heterogeneity on Webcam Sex Platforms
Jokubauskaitė, E., Rieder, B., & Burkhardt, S. (2023) · Social Media + Society · doi:10.1177/20563051231214807 · Cited by 15
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BackgroundThomas, A., Archer, A., & Engelen, B. (2024) · Oxford University Press eBooks · doi:10.1093/oso/9780197781722.003.0004 · Cited by 12
Music interaction research in HCI
Liikkanen, L. A., Amos, C., Cunningham, S. J., et al. (2012) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/2212776.2212401 · Cited by 12
Stevens, J. H., & Berlan, E. D. (2014) · Perspectives on Sexual and Reproductive Health · doi:10.1363/46e0614 · Cited by 14
Risk behavior in decision-making in a multi-person-setting
Fenzl, T., & Brudermann, T. (2009) · The Journal of Socio-Economics · doi:10.1016/j.socec.2009.06.001 · Cited by 12
A Real-Time Social Media Aggregation Tool: Reflections From Five Large-Scale Events
Rogstadius, J., Kostakos, V., Laredo, J., et al. (2011) · doi:10.5281/zenodo.1117402 · Cited by 12
Pan, X., Hou, L., & Liu, K. (2017) · PLoS ONE · doi:10.1371/journal.pone.0175761 · Cited by 13
Learnings from deploying a voice-based social platform for people with disability
Dubey, K., Gupta, P., Shriwas, R., Gulvady, G., & Sharma, A. (2019) · ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS) · doi:10.1145/3314344.3332503 · Cited by 12
“This reveals the interplay between position bias and relevance for SGC and UGC posts on Enable Vaani, similar to the results on online search and recommendation systems [4,10,19].”
BackgroundResultWang, C.-J., & Zhu, J. J. H. (2021) · Internet Research · doi:10.1108/intr-08-2019-0313 · Cited by 8
Biases in Recommendation System
Bhadani, S. (2021) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3460231.3473897 · Cited by 15
“For MusicLab, the quality and popularity values were available in the dataset but for MovieLens and Amazon they were not, therefore I estimated the quality and popularity using the user ratings.”
BackgroundMethodInfluentialSocial Influence and Evolution of Market Share
Ceyhan, S., Mousavi, M., & Saberi, A. (2011) · Internet Mathematics · doi:10.1080/15427951.2011.579849 · Cited by 12
“Both of these results are consistent with the observations made in the experimental work [Salganik et al. 06].”
InfluentialUncovering and Predicting Human Behaviors
Cui, P., Liu, H., Aggarwal, C. C., et al. (2016) · IEEE Intelligent Systems · doi:10.1109/mis.2016.37 · Cited by 12
Cohen, S. K., Hsu, S. T., & Dahlin, K. (2016) · Organization Science · doi:10.1287/orsc.2016.1063 · Cited by 12
“In experimental work, Salganik et al. (2006) found that social influence, exerted through anonymous contact with peers, determined which songs became hits.”
BackgroundListeners' and Performers' Shared Understanding of Jazz Improvisations
Schober, M. F., & Spiro, N. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.01629 · Cited by 12
“…contexts can be affected by the reactions of those around them (see e.g., Pennebaker, 1980; Mann et al., 2013), and listeners’ evaluations of the quality of music in an online marketplace can be affected by their knowledge of the evaluative judgments of other listeners (Salganik et al., 2006).”
BackgroundPower Laws and Market Shares: Cumulative Advantage and the Billboard Hot 100
Haampland, O. (2017) · Journal of New Music Research · doi:10.1080/09298215.2017.1358285 · Cited by 12
“This ‘ a bit of both ’ notion also corresponds with results from large-scale experiments that found that ‘ 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 ’ (Salganik et al., 2006, p. 854).”
Pachur, T., Schooler, L. J., & Stevens, J. R. (2012) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195388435.003.0007 · Cited by 12
Chen, W. J., Ting, T.-T., Chang, C.-M., Liu, Y.-C., & Chen, C. (2013) · Journal of Food and Drug Analysis · doi:10.1016/j.jfda.2013.09.044 · Cited by 12
Predicting the popularity of topics based on user sentiment in microblogging websites
Wang, X., Wang, C., Ding, Z., et al. (2017) · Journal of Intelligent Information Systems · doi:10.1007/s10844-017-0486-z · Cited by 12
“[26] answered the question experimentally by measuring the impact of content quality and social influence on the popularity of cultural artifacts like”
BackgroundCollective Problem Solving in NetworksPreprint
Mason, W., & Watts, D. J. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1795224 · Cited by 12
Self-citationPreference Markets: Organizing Securities Markets for Opinion Surveys with Infinite ScalabilityPreprint
Dahan, E., Soukhoroukova, A., & Spann, M. (2007) · SSRN Electronic Journal · doi:10.2139/ssrn.1031754 · Cited by 11
User-Generated Content and Competing Firms' Product DesignPreprint
Kwark, Y., Chen, J., & Raghunathan, S. (2014) · SSRN Electronic Journal · doi:10.2139/ssrn.2492840 · Cited by 12
Mohammadi, A., & Shafi, K. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2752085 · Cited by 15
“The true value however may not be known or well defined ex ante (typical examples in prior research include the “cultural” markets of musical tastes (Salganik, Dodds, and Watts, 2006) or artistic projects (Nanda and Mollick, 2016)), which renders the expert judgement the next best alternative.”
BackgroundSequential Voting Promotes Collective Discovery in Social Recommendation SystemsPreprint
Celis, L. E., Krafft, P. M., & Kobe, N. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.04466 · Cited by 13
Althoff, T., Jindal, P., & Leskovec, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.03053 · Cited by 42
“Recent research has made great advancements towards understanding of fundamental structural properties [36, 41], growth [35], navigability [31, 37], community structure [11, 50], information diffusion [12, 19], influence maximization [30], social capital [17, 27], and social influence [47] in online social networks.”
BackgroundPublic Discourse in the Web Does Not Exhibit Group PolarizationPreprint
Wu, F., & Huberman, B. A. (2007) · SSRN Electronic Journal · doi:10.2139/ssrn.1052321 · Cited by 12
Understanding investor co-investment in a syndicate on equity crowdfunding platforms
Wu, J., Liu, L., & Cao, Y. (2023) · Industrial Management & Data Systems · doi:10.1108/imds-09-2022-0538 · Cited by 11
Income distribution and nudity on social media: Attention economics of Instagram stars
Gaenssle, S. (2023) · Kyklos · doi:10.1111/kykl.12363 · Cited by 11
Learning from Outliers and Anomalies
Ruef, M., & Birkhead, C. (2024) · Academy of Management Perspectives · doi:10.5465/amp.2023.0481 · Cited by 11
Predictability of 'Unpredictable' Cultural Markets
Colbaugh, R., & Glass, K. (2010) · Cited by 11
The Evaluative Advantage of Novel Alternatives
Mens, G. L., Kareev, Y., & Avrahami, J. (2015) · Psychological Science · doi:10.1177/0956797615615581 · Cited by 11
Death of the Salesman but Not the Sales Force: How Interested Promotion Skews Scientific Valuation
Azoulay, P., Wahlen, J. M., & Zuckerman, E. W. (2019) · American Journal of Sociology · doi:10.1086/706800 · Cited by 11
“2014) or by anonymous peers (Salganik et al. 2006; van deRijt 2019)—they can skew valuations to produce informational inefficiency.”
BackgroundInfluentialRelevance in Web search: between content, authority and popularity
Олейник, А. (2021) · Quality & Quantity · doi:10.1007/s11135-021-01125-7 · Cited by 11
“The availability of the information on the other users’ choices has an impact on the ranking of items by the user (Salganik et al. 2006, p. 856).”
BackgroundHampton, K. N., & Chen, W. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.056 · Cited by 11
Detection of Phase Transition in Generalized Pólya Urn in Information Cascade Experiment
Hino, M., Irie, Y., Hisakado, M., et al. (2016) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.85.034002 · Cited by 11
Yen, Y. (2013) · International Conference on Consumer Electronics, Communications and Networks (CECNet) · doi:10.1109/cecnet.2013.6703427 · Cited by 11
“For example, Salganik et al. [19] 978-1-4799-2860-6/13/$31.00 ©2013 IEEE found that people might form completely different evaluations of the same song due to the perception of others’ behavior.”
BackgroundInfluentialWhat differentiates Internet shoppers from Internet surfers?
Izquierdo‐Yusta, A., Martínez‐Ruiz, M. P., & Herranz, A. Á. (2014) · Service Industries Journal · doi:10.1080/02642069.2014.871536 · Cited by 11
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Schäfer, T., Auerswald, F., Bajorat, I. K., Ergemlidze, N., Frille, K., Gehrigk, J., Gusakova, A., Kaiser, B., Pätzold, R. A., Sanahuja, A., et al. (2015) · Musicae Scientiae · doi:10.1177/1029864915622054 · Cited by 10
Des données du Web pour faire de la sociologie… du Web ?
Beuscart, J.-S. (2017) · Collège de France eBooks · doi:10.4000/books.cdf.5009 · Cited by 10
Quantifying the Impact of Cognitive Biases in Question-Answering Systems
Burghardt, K., Hogg, T., & Lerman, K. (2018) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v12i1.15042 · Cited by 11
On the naturalistic relationship between mood and entertainment choice.
Ersner-Hershfield, H., & Alter, A. L. (2019) · Journal of Experimental Psychology Applied · doi:10.1037/xap0000220 · Cited by 12
Przepiorka, W., & Aksoy, O. (2020) · Social Forces · doi:10.1093/sf/soaa057 · Cited by 11
“Herding is known under different names (e.g., social learning) and can lead to the so-called success-breeds-success dynamics and growing inequality on the supply side (DiPrete and Eirich 2006; Salganik et al. 2006; van de Rijt et al. 2014).”
BackgroundInfluentialThe Ecology of Management Concepts
Denrell, J. C., & Kovács, B. (2020) · Strategy Science · doi:10.1287/stsc.2020.0108 · Cited by 11
Mediennutzung in Zeiten von Corona
Spitzer, M. (2020) · Nervenheilkunde · doi:10.1055/a-1193-8248 · Cited by 12
“Die unabhängigen Bewertungs- und Entscheidungsfindungsprozesse, welche eine Grundlage unserer Demokratie sind, können aber nur funktionieren, wenn es Privatheit gibt – auch dies wurde empirisch nachgewiesen [36].”
BackgroundThe Enormous Flock of Homophily Researchers: Assessing and Promoting a Research Agenda
McPherson, M., Smith‐Lovin, L., & Rawlings, C. M. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.033 · Cited by 12
The Is and Ought of Sharing: The Equality Heuristic Across the Lifespan
Keller, M., Gummerum, M., Canz, T., Gigerenzer, G., & Takezawa, M. (2012) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195388435.003.0006 · Cited by 12
An Agent-Based Modeling Analysis of Helpful Vote on Online Product Reviews
Liu, Q. B., & Karahanna, E. (2015) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2015.192 · Cited by 14
“In a series of web-based experiments, Salganik and colleagues (2006, 2008, and 2009) showed that almost any music of average quality may become a “hit” if social influence is introduced by showing the number of previous downloads [9, 10, 11].”
The social structure of celebrity: an empirical network analysis of an elite population
Ravid, G., & Currid‐Halkett, E. (2013) · Celebrity Studies · doi:10.1080/19392397.2013.791047 · Cited by 11
“…disproportionate popularity of and economic reward appropriated to cultural producers (musicians, actors and so forth) who exhibit marginally more talent at the outset, a process similarly conceptualised in the ‘Matthew effect’ (Merton 1968) and later empirically tested by Salganik et al. (2006).”
BackgroundMass media, the ‘sensational message’, and metamorphic truths
Vista, A. (2014) · Telematics and Informatics · doi:10.1016/j.tele.2014.05.005 · Cited by 11
“In social networks, where trends are a significant influence among young members (see for example Salganik et al., 2006), this amplification of viral trends towards uniform drifts may have unforeseen consequences.”
BackgroundTrimble, V., Aschwanden, M. J., & Hansen, C. J. (2007) · Space Science Reviews · doi:10.1007/s11214-007-9224-0 · Cited by 17
“We were scolded for trying to do this last year, though not by Nature. o Discovery of a bandwagon effect in success of new music (Salganik et al. 2006) through a controlled experiment in downloads.”
BackgroundA Field-Experimental Study of Emergent Mobilization in Online Collective Action*
Vaillant, G. G., Tyagi, J., Akın, A. İ., Poma, F. P., Schwartz, M. K., & van de Rijt, A. (2015) · Mobilization An International Quarterly · doi:10.17813/1086-671x-20-3-281 · Cited by 11
Rank-Based Groupings and Decision Making
Keefer, Q. A. W. (2014) · Journal of Sports Economics · doi:10.1177/1527002514541448 · Cited by 11
Boussard, V., Godechot, O., & Woloszko, N. (2017) · Socio-Economic Review · doi:10.1093/ser/mwx056 · Cited by 11
Predicting the decay of collective memory
Coman, A. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0480-7 · Cited by 10
Attention Inequality in Social Media
Zhu, L., & Lerman, K. (2016) · arXiv · doi:10.48550/arxiv.1601.07200 · Cited by 13
“Current consensus holds that inequality cannot be explained by variation in quality of the work or individual talent (Adler 1985; Salganik, Dodds, and Watts 2006; Lariviere and Gingras 2010).”
BackgroundThe Myopia of Crowds: A Study of Collective Evaluation on Stack ExchangePreprint
Burghardt, K., Alsina, E. F., Girvan, M., Rand, W. M., & Lerman, K. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2736568 · Cited by 12
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
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