Web-based experiments for the study of collective social dynamics in cultural markets

By Matthew J. Salganik, Duncan J. Watts. 2009

134 citations recorded by the source corpus.

Social scientists are often interested in understanding how the dynamics of social systems are driven by the behavior of individuals that make up those systems. However, this process is hindered by the difficulty of experimentally studying how individual behavioral tendencies lead to collective social dynamics in large groups of people interacting over time. In this paper we investigate the role of social influence, a process well studied at the individual level, on the puzzling nature of success for cultural products such as books, movies, and music. Using a “multiple-worlds” experimental design we are able to isolate the causal effect of an individual level mechanism on collective social outcomes. We employ this design in a web-based experiment in which 2,930 participants listened to, rated, and download 48 songs by up-and-coming bands. Surprisingly, despite relatively large differences in the demographics, behavior, and preferences of participants, the experimental results at both the individual and collective level were similar to those found in Salganik, Dodds, and Watts (2006). Further, by comparing results from two distinct pools of participants we are able to gain new insights into the role of individual behavior on collective outcomes. We conclude with a discussion of the strengths and weaknesses of web-based experiments to address questions of collective social dynamics.

Subfields: Safety Research, Sociology and Political Science, Statistical and Nonlinear Physics

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Citations in the corpus

Leveraging Position Bias to Improve Peer Recommendation (2014, background): “[2,21], which examined why some cultural artifacts become vastly more popular than others, and why their popularity is largely unpredictable.”
Quantifying Social Influence in an Online Cultural Market (2012, background): “Songs were shown in random order in the independent condition [1,6,7].”
Taming the Unpredictability of Cultural Markets with Social Influence (2017, background): “Unpredictability and inequality of product market share suggest that some high-quality products may go unnoticed [19, 21] on the one hand, while lower-quality ones may be oversold [11, 17] on the other.”
Aligning Popularity and Quality in Online Cultural Markets (2021, result): “In particular, Salganik et al suggested that social influence contributes to unpredictability, inefficiencies, and inequality of success, with follow-up experiments confirming these initial findings (Salganik and Watts 2009; Muchnik, Aral, and Taylor 2013; van de Rijt et al. 2014).”
The Benefits of Social Influence in Optimized Cultural Markets (2015, result): “In particular, Salganik et al suggested that social influence contributes to both unpredictability and inequality of success, with follow-up experiments confirming these initial findings [9–12] (see also [13] for a perspective on the MUSICLAB paper).”
Reframing the Decision-Makers’ Dilemma: Towards a Social Context Model of Creative Idea Recognition (2017, background): “…objective bases for decision making, we suggest that decision makers are particularly likely to attend to social approval cues as these cues appear to provide evidence of acceptability (Cialdini, 1993), legitimacy (Rao, Chandy, & Prabhu, 2008), and potential for success (Salganik & Watts, 2009).”
Digital Footprints: Opportunities and Challenges for Online Social Research (2014, background): “This possibility was demonstrated for the first time by Salganik and colleagues (Salganik & Watts 2009, Salganik et al. 2006) in an experiment that has become an instant classic.”
Crowdsourcing graphical perception: using mechanical turk to assess visualization design (2010, background): “Web-based experimentation is increasingly popular and accepted in social psychology [16], including research on the development of cultural markets [25] and the manipulation of incentives for online peer-production [4, 18].”
Disentangling the Effects of Social Signals (2014, background): “ch get richer” phenomenon. Experimental studies of cultural markets concluded that social inﬂuence signals lead to large inequality and unpredictability of collective outcomes (Salganik et al., 2006; Salganik and Watts, 2009). However, before information about others’ preferences, can inﬂuence a per”